IOT Stream Data Mining

GALLITELLI Davide - A.Y. 2017/18 @ TELECOM ParisTech

Goal

These are some notes for the course IOT Steam Data Mining, held by prof. BIFET Albert, at TELECOM ParisTech during the second period of the first semester of the 2017/18 academic year.

Ideally, these would comprehend everything explained during the course, as well as some further insight into some of the topics.

Everything in here is based on my understanding of the topic, therefore some things may be explained quickly or not in depth enough.

Enjoy.

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Introduction

In the Big Data world, data is becoming increasingly huge and fast. This trend will not decrease, but it will become even stronger, considering how IOT devices are becoming mainstream.

Traditional discovery methods of KDDs are based on batches: data is read from the input, it's held in memory and a model is built from the memory itself. These methods work pretty well with non-huge datasets, since memory is a constraint even for the biggest of the clusters. IOT devices will flood data centers with new inputs generated continuously and from many different sources. This calls for a new method of handling knowledge discovery.

Stream mining proposes a new approach to knowledge discovery, an incremental one base on instances. Basically, examples are read as soon as they arrive, one instance at a time, and at most once and learning is applied to this instance by updating the existing model. An instance processes one example at a time, therefore requiring only a limited amount of memory and a limited amount of time. Furthermore, this new KDD should enable anytime prediction*, which means that at any time during the learning process I should be able to obtain predictions from the model.

As stated before, due to the speed at which new instances arrive, these examples should be read and analyzed fast, which means never more than once. This implies that we are looking for an approximation of the solution, meaning that we want, with high probability (1-delta) a small error epsilon with respect to the classic batch solution which would require too much time to compute (due to multiple passes over the data). Most of the algorithms have parameters that can be fine tuned so that a certain degree of confidence can be enforced, at the expenses of computation time.

In a nutshell, stream data mining comprehends:

• potentially unbounded data (streams can be infinite)
• very fast incoming stream (requiring at-most-once read)
• time and memory constrained learning models (should be fast in learning and always reside in main memory)
• anytime prediction (always available model)

Some application examples:

• IOT sensor analysis and prediction
• Market/Stock real-time analytics
• Real-time social network mining for sentiment analysis
• Time series prediction

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Data Stream Algorithms

In order to allow this probabilistic approach to knowledge discovery, some new algorithms are required. Those should guarantee, with a tunable parameter of confidence, certain performances in terms of errors. In general, we are looking for small error rate with high probability, which means mathematically that:

8-bit counting: the Morris Approximate Counting Algorithm

The very first time something close to the idea behind Data Stream Algorithm has been talked about dates back to 1978 with a paper from Robert Morris named "Counting Large Numbers of Events in Small Registers".

The main idea behind it was that, given a 8-bit register, we want to store the highest possible number of numbers, or events. With the normal binary system applied to the standard decimal counting, we can store up to 2^8 -1 numbers, or 255 numbers.

If we used instead a logarithmic scale, then the number of events that can be stored in 8 bits would greatly increase:

In order to implement such strategy, Morris came up with the approximate counting algorithm:

1. Init counter c <- 0
2. for every event in the stream
3. 		do rand = random number between 0 and 1
4. 			if rand < p
5. 				then c <- c + 1

where p is a parameter of the algorithm.

Basically, the algorithm tries increases the counter with a probability p:

• If p = 2^(-1) = 1/2, we can store up to 2 x 256 values, with standard deviation sigma = sqrt(n)/2.
• If p = 2^(-c), the error of the approximation is E[2^c] = n+2 with variance sigma^2 = n(n+1)/2
• Given a number b, if p = b^(-c), then E[b^c] = n(b-1)+b with variance sigma^2 = (b-1)n(n+1)/2.

Count-Distinct algorithm: Flajolet-Martin Probabilistic Counting

The Flajolet–Martin algorithm is an algorithm for approximating the number of distinct elements in a stream with a single pass, exploiting the logarithmic representation seen before. The algorithm was introduced by Philippe Flajolet and G. Nigel Martin in their 1984 article "Probabilistic Counting Algorithms for Data Base Applications".

By definition of the problem, we can infer that:

if the stream contains n elements with m of them being unique, then the algorithm runs in O(n) time and need O(log(m)) memory.

Given the pseudo-code above, the algorithm is based on a hash function h(x) to represent the incoming example. Such hash function maps the example x to integers in the range [0;2*L -1], with the outputs being sufficiently uniformly distributed. Then, the position of the least significant 1-bit (or, the left-most [*] bit set to 1) is used to update a bitmap representing the "buckets" of the hash function (which has length L and initially contains all 0s). Finally, the position b of the left-most zero in the bitmap is considered. Because it can be stated that, with logarithmic distribution:

[EN] for me, how this error computation is made is magic. Any help is welcome.

then we return the approximate value given b index of the bitmap. Worst case scenario, the error is +-1.12 .

[*] : everybody knows that the least significant bit should be counted starting from the right. In this example, it was not such a case, bear with it. The concept does not change, only the position in the bitmap would change.

Example:

item x	hash(x)	p(hash(x))	bitmap

Let's start with an empty table. The first example incoming is a, which is magically-hashed to 0110. It does not matter how the hashing function generates the binary string, as any can be used.

item x	hash(x)	p(hash(x))	bitmap
a	0110

p(hash(x)) computes the position of the left-most bit set to 1, therefore in this case p(hash(x)) = 1. Then, bitmap[p(hash(x))] is updated (considering the left-most bit as bitmap[0]).

item x	hash(x)	p(hash(x))	bitmap
a	0110	1	0100

Following the same idea, further examples are handled like this:

item x	hash(x)	p(hash(x))	bitmap
a	0110	1	0100
b	1001	0	1100
c	0111	1	1100
a	0110	1	1100
b	1001	0	1100
d	1100	0	1100
e	0001	3	1101
f	1010	0	1101

[EN] the record in bold chars has been changed from the slides example, to show a third bit switch

With this, we can compute at any time the count of unique elements in the stream. Since the left-most bit set to 0 in the bitmap b = 2, our estimation of unique numbers n = (2^b)/0.77351 = 5.17. The true n is 6, but it is still a good approximation.

Example 2:

If we had a situation like this:

item x	hash(x)	p(hash(x))	bitmap
a	0110	1	0100
a	0110	1	0100
a	0110	1	0100
e	0001	3	0101

Then we would obtain:

• b = 0
• n = 2^0 / 0.77351 = 1.29

A modified FM Algorithm

A modified version of the same algorithm used the max function instead of a bitmap.

Count-Distinct: HyperLogLog

The FM algorithm explained above has been refined in "LogLog counting of large cardinalities" and "HyperLogLog: The analysis of a near-optimal cardinality estimation algorithm".

In this algorithm, the stream is divided in m = 2^b substreams, and the estimation uses harmonic mean[*]. This way, the realtive accuracy is greater than the stochastic averaging[**], and it's equal to 1.04 / sqrt(m).

[*] Harmonic Mean: it's one of different kinds of averages, typically used when the average of rates is desired. It's the reciprocal of the arithmetic mean of the reciprocals of a given set, therefore: Hm = len(X) / sum(1 / x_i) where X is the array of examples

[**] Stochastic Averaging: when performing m experiments in parallel, the standard deviation can be averaged as st_dev' = st_dev/sqrt(m), with relative accuracy of 0.78/sqrt(m).

Frequent Itemset Mining: MAJORITY Algorithm

In the frequent itemset mining problem, we are looking to build a dataset of frequent itemset while reading a stream. There are different algorithms to do so.

[MAJORITY]
1 Init counter c <- 0
2 for every item s in the stream
3	if counter is zero
4 		then pick up the item
5 	if item is the same
6 		then increment counter
7 	else decrement counter

The easiest of them is the Majority algorithm, which increments a counter related to the item read from the stream.

Frequent Itemset Mining: FREQUENT Algorithm

[FREQUENT]
1 for every item i in the stream
2 	if item i is not monitored
3 		if < k items monitored
4 			then add a new item with count 1
5 		else if an item z whose count is zero exists
6 			then replace this item z by the new one
7 		else decrement all counters by one
8 	else item i is monitored
9 		increase its counter by one

A different version instead is the Frequent algorithm, which only keeps monitoring the k-most frequent elements. Whenever an example is read from the stream, its counter is updated if it's not new, otherwise, the list of monitored items is updated as specified in the snippet above.

Frequent Itemset Mining: LOSSYCOUNTING Algorithm

The Class Version

The LossyCounting algorithm is based on the concept of periodically removing items with low frequency from the list of monitored items. Every time a new item is read, its counter gets updated

• by 1 if it's an already monitored item
• by 1 + delta , where 0 < delta < floor(n/k), if it's a new item

Once the update is done, then the counters are all decremented by one, and the elements with frequency equal to zero are removed.

Given f threshold frequency, the algorithm will need at most K = 1/f * log(f*N) counters, and a window of W = 1/e elements.

The Paper/Internet Version

The main concept is still the same, but the idea is to divide the stream into windows ([EN] maybe n/k?). Every time a new window is read, the counter for the item is updated by the frequency in the window. Once the full window is read, then all counters are decremented by one. The algorithm is then iterated until the end of the stream.

The new algorithm would more or less look like so:

[CUSTOM LOSSYCOUNTING]
1 	define window size W
2	define window counter C
3 	for every item i in the stream S
4 		if item i is monitored
5			increase counter of item i by 1
6		else new item with count (#window) +1
7		increase C by 1
8		if C = W
9			decrement all counters by 1
10			remove items with 0 count
11			reset C to 0

The actual count of each item depend on the window size. Still, it is possible to find the same frequent itemset; if stream-size = N and window-size = W = 1/e, then the frequency error is f_e = eN.

Example:

Let's apply the LOSSYCOUNTING algorithm to the following stream:

a	b	c	d	a	c	a	a	a	c	d	c	a	a	c	b

STEP 0

Let's define a window size W = 4 (since we have 16 elements, this way the example is easier). At the beginning, the window counter is C = 0.

STEP 1

The first window to be analyzed is:

a	b	c	d

All of them are new items, therefore the frequent itemset list becomes, before decrementing it:

a	b	c	d
1	1	1	1

Since we reached window boundary, all counters are decremented by one:

a	b	c	d
0	0	0	0

All items are dropped from the list.

STEP 2

a	c	a	a

We're at iteration 1, DELTA = 1. The itemset list becomes:

a	c
3+1	1+1

After the update:

a	c
3	1

STEP 3

a	c	d	c

We're at iteration 2, DELTA = 2. The itemset list becomes:

a	c	d
4	3	1+2

After the update:

a	c	d
3	2	2

STEP 4

a	a	c	b

We're at iteration 3, DELTA = 3. The itemset list becomes:

a	c	b	d
5	3	1+3	2

After the update:

a	c	b	d
4	2	3	1

For this example, we have stream-size = N = 16 and window-size = W = 4 = 1/e --> e = 1/4 = 0.25, the frequency error = f_e = 16*0.25 = 4.

Frequent Itemset Mining: SPACE SAVING Algorithm

Space Saving can be considered as a merge of LossyCounting and Frequent algorithms. Instead of dropping low count items, just like in the LossyCounting algorithm, the Space Saving algorithm replaces the item with the lowest counter with the just found not monitored item, and then increments its counter by 1.

Example:

Let's suppose that we have an itemset count as such:

a	b	c
7	5	3

then, we read input d. The itemset list has reached maximum capacity (set before starting). What happens? The d input replaces the item with the lowest count, which is c. The itemset list becomes:

a	b	d
7	5	4

Frequent Itemset Mining: COUNT-MIN SKETCH Algorithm

The counter algorithms analyzed until now only handle the “arrivals only” model, not the “arrivals and departures” one. This means that, if there is a case of "negative frequency", the previous algorithms do not have a deterministic solution. Sketch algorithms compute a summary that is a linear transform of the frequency vector.

The COUNT-MIN algorithm is one of such algorithms. It uses a two-dimensional array with width w = ceil(e/epsilon) and depth d = ceil(ln(1/delta)), where both epsilon and delta are user-given parameters which tune the performances of the algorithm, since the depth represents also the update time of the algorithm (the time it takes to read the column/array of values). NB: e in the width formula is the Euler's number e = 2.718281.

In a nutshell, CM-Sketch computes frequency data adding and removing real values. Every time it reads an input (j,+c), where c is a real value, for each row i of the table, apply the corresponding hash function h_i to obtain a column index k = h_i(j). Then increment the value in j,k (with j = [1...d] and k = [1...w]) by +c. In order to compute the frequency f then, it is as simple as computing the minimum of those updated values, which means f = min[i,h_i(j)].

Given the epsilon parameter, it is guaranteed that this algorithm provides a frequency f <= f_r + epsilon*N, where f_r is the real frequency and N is the stream size.

Reservoir Sampling

Finally, given a data stream X, we want to choose k items with the same probability, storing only k elements in memory.

[RESERVOIR SAMPLING]
1 	for every item i in the first k items of the stream
2 		store item i in the reservoir
3 	n <- k
4 	for every item i in the stream after the first k items of the stream
5 		select a random number r between 1 and n
6 		if r < k
7 			then replace item r in the reservoir with item i
8 		n = n + 1

The problem of MEAN and VARIANCE

Given a data stream X, computing statistics such as mean or variance loses part of the meaning: these computations have to be done on a sliding window of n element, since mean and variance are based on the division operator and therefore is not cumulative.

We can maintain simple statistics over sliding windows, using O(1/epsilon * log^2(N)) space, where:

• N is the length of the sliding window
• epsilon is the accuracy parameter

Exponential Histograms

Exploiting the logarithmic compression of values is very useful when storing a window into memory. This is done by partitioning the content of the data stream sliding window into buckets of exponentially-growing size. This means that the size of the buckets follows a 2^n pattern, with n starting as 0. The number of buckets of the same size is controlled by a parameter M. In order to give answers in O(1) time, three counters are kept: LAST, TOTAL and VARIANCE.

Given a window of W elements, and e maximum error (user-controllable parameter), then:

• the parameter M is obtained as M = 1/(2e)
• the total number of buckets is M*log(W/M)

Example: how buckets are generated

Let's consider this stream W:

1110001010101

Suppose that the first bit to have arrived is the left-most bit of the given stream. The maximum error is e=1/6=0.1667. How is this window stored in memory? What happens when a new 1 bit arrives?

If e=1/6, then M=3, meaning that we can at most 3 buckets of the same size. In order to see the buckets, let's start unraveling the stream.

The first 1 arrives. It is put in its own bucket. Then other two 1s arrive, each is put in its own bucket.

1	1	1	0001010101

Then, a 0 arrives. There can't be another bucket with size 1, therefore we need to compress the buckets with size 1, starting from the left-most one. It becomes:

11	1	0	001010101

One more iteration:

11	1	0	0	01010101

With the next 1, we have the same problem as before: we compress.

11	10	0	0	1010101

In three iterations, we will have the following situation:

11	10	00	1	0	1	0101

With the new 0 incoming, just like before we would need to compress two buckets of 1 size into a 2-sized bucket. But we reached the M threshold for them too, therefore two two-sized buckets (the left-most ones) will be compressed to a 4-sized bucket.

11	10	00	10	1	0	101

1110	00	10	1	0	101

With the next three iterations, we would obtain:

1110	00	10	1	0	1	01

1110	00	10	10	1	0	1

What if another 1 arrives?

1110	00	10	10	1	0	1	1

1110	0010	10	10	1	1

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Concept Drift

In the domain of stream analytics, sometimes the data and its properties change in unforeseen ways, making the predictions given by the previously built model less accurate. Therefore, there is a need for incremental learning, which implies either updating the model once the change has been detected (left schema on the following image), or using a more complex algorithm, possibly made of multiple estimators (right schema on the following image).

Let's consider a standard learning algorithm, with only one estimator. Our goal is to include a way to detect a distribution change by means of some kind of alarm, as well as a way to generate a new prediction which minimizes the prediction error. This situation can be represented by the following schema of a concept-drift-handling algorithm.

Change detection is a tricky business. There may be cases in which false alarms are launched, and those may be caused by noise in the data or short-time changes in the distribution. Once could say that the design of a change detector is a compromise between detecting true changes and avoiding false alarms.

Some metrics can be defined to evaluate concept drift:

• Mean Time between False Alarms (MTFA)
• Mean Time to Detection (MTD)
• Missed Detection Rate (MDR)
• Average Run Length (ARL(θ))

Ideally, an optimal change detector and predictor system should have:

• High accuracy in the prediction
• Low mean time to detection (MTD), false positive rate (FAR) and missed detection rate (MDR)
• Low computational cost: minimum space and time needed
• Theoretical guarantees
• No parameters needed

Detecting concept drift: the CUSUM Test

The cumulative sum (CUSUM algorithm), gives an alarm when the mean of the input data is significantly different from zero. It's a memoryless test, and its accuracy depends on the choice of parameters υ and h. Mreover, it doesn't handle negative values for the instance read.

Detecting concept drift: the Page Hinckley Test

Unlike the CUSUM algorithm, it is possible to have negative values.

Detecting concept drift: the Geometric Moving Average Test

The forgetting factor λ is used to give more or less weight to the last data arrived. The greater the λ, the greater the weight of the most recent value. The treshold h is used to tune the sensitivity and false alarm rate of the detector.

Detecting concept drift: the Statistical Test

Note to the reader: this part is taken from the MOA book: "Data Stream Mining: A Practical Approach". I found most of it pretty easy to understand, therefore I copy-pasted it the first chunk of it.

CUSUM and GMA are methods for dealing with numeric sequences. For more complex populations, we need to use other methods. There exist some statistical tests that may be used to detect change. A statistical test is a procedure for deciding whether a hypothesis about a quantitative feature of a population is true or false. We test an hypothesis of this sort by drawing a random sample from the population in question and calculating an appropriate statistic on its items. If, in doing so, we obtain a value of the statistic that would occur rarely when the hypothesis is true, we would have reason to reject the hypothesis.

Given two sources of data, x_0 and x_1, we want to test the hypothesis H_0, e.g. that the two sources come from the same distribution. To do so, we need two statistical properties, such as two estimates, µ_0 and µ_1, and the variances σ_0 ^2 and σ_1 ^2. If there is no change in the data, these estimates will be consistent. Otherwise, a hypothesis test will reject H0 and a change is detected. An easy way to test this consistence is to compute the difference of the estimates: or with the χ^2 test:

Example:

For example, suppose we want to design a change detector using a statistical test with a probability of false alarm of 5%, that is:

Since, with Gaussian Distribution, P(X < 1.96) = 0.975 the test becomes: test with a probability of false alarm of 5%, that is:

Change detection: ADWIN (ADaptive data stream sliding WINdow)

ADWIN is a parameter-free adaptive size sliding window, with theoretical guarantees.

ADWIN is an adaptive sliding window algorithm, whose window size is recomputed online according to the rate of change observed.

It keeps an adaptive sliding window which tries to estimate the mean of a monitored numeric variable. The window grows while things are stable, while, upon a change of the mean, the window is cut into two parts: an "old" part, which is discarded, and a "new" part, with which now has a new mean.

The size of the window is not a problem, since storing of the window is handled by an internal exponential compression scheme, basically exponential histograms (see exponential histograms example above).

Since ADWIN algorithm uses the Data Stream Sliding Window Model:

• It tries O(log(W)) cutpoints
• It uses O(M*log(W/M)) memory words (assuming a memory word can contain numbers up to W).
• It can process the arrival of a new element in O(1) amortized time and O(log(W)) worst-case time.
• It can provide the exact counts of 1’s in O(1) time per point.

It is not a purely heuristical algorithm, because it has theoretical guarantees on the rate of false positives/negatives, as well as on the relation of the window size and change rates.

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Evaluation

The evaluation procedure of a learning algorithm determines which examples are used for training the algorithm, and which are used to test the model output by the algorithm. With the new incremental approach to the learning process, a new definition of accuracy over time is needed, as well as new evaluation frameworks.

Error Estimation

Two methods exists for error estimation:

• Hold-out
  • to be used only if a testing dataset is available, it consists of applying an unbiased estimator to the test set at regular time intervals
  • it is generally more accurate
• Prequential/Interleaved-Test-Then-Train
  • if there is no testing data available, then this is the go-to method
  • For each example in the stream, the actual model makes a prediction, and then uses it to update the mode - a bootstrap batch learner is needed!
  • it's the cross-validation of the data streams

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Classification

In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

Different kind of classification problem exist:

• binary classification, where given an input we want to predict one binary value for one label
  • y(x) = {0/1}
• multi-class classification, where the prediction consists of choosing (read, setting to true) only one of multiple labels
  • y(x) = {1,0,0} v {0,1,0} v {0,0,1} [only one of those solution is ok, every comma separates two labels]
• multi-label classification, where each label can have its binary value (multiple labels can be set)
  • y(x) = any combination of 3 bits, 2³ possible prediction targets
• multi-output/multi-dimensional classification, where the output is multiple outputs for multiple labels

The classification problem can then be defined as:

Given a set of L labels, we wish to obtain a model h that xan take an input x and produce a prediction y=h(x).

Multi-label classification

The algorithms analyzed in previous courses were mostly single-label (binary or multi-class), which can be graphically represented as:

So, how it is possible to obtain a multi-label prediction, without having to come up with brand new and very problem-specific models?

Solution 1 : Binary Relevance - Classifier Chain

One could think of considering a multi-label problem as a composition of multiple binary problems, by using each input to predict a different label. This means transforming the dataset, composed of N instances and L labels to predict, into L separate binary problems, one for each label:

However, this solution assumes independence among target labels, which is an hypothesis that generally does not hold in the representation of the knowledge we have of the world. In most cases, .

Solution 2 : A Probabilistic Cascade - Probabilistic Classifier Chain

Given an instance x, dependence among target labels can be represented as a probabilistic chain:

A probabilistic classifier chain (formula above) tries to estimate the conditional distribution p(y|x) using the chain rule of probabilities. Learning a multilabel classifier is reduced to learning K independent probabilistic binary classifiers. These independent base classifiers may be, for example, logistic regression models with a specialized feature representation.

Among the advantages of the PCC approach:

• it is a decomposition method, therefore:
  • it leverages existing binary models
  • it is computationally inexpensive
  • the decomposition itself is probabilistically motivated
• it does not require any prior transformations of the dataset
• it is a principled probabilistic model with a theoretical understanding of how it may be used to produce Bayes optimal predictions for a variety of loss functions (EN: don't ask, just copy. Took from Learning and Inference in Probabilistic Classifier Chains with Beam Search )

Solution 3 - Label-PowerSet approach

Both methods above try to estimate/learn the probabilities P(y_j|x,y_1,...y_j-1) individually, by decomposing the multi-label problem into multiple binary classification problems. However, it is possible to try and estimate P(y|x) directly, where the target space Y contains all the possible 2^L distinct combinations of the label values: this is a powerset of the solution, therefore this is the powerset approach.

It is sufficient to transform the dataset from having multiple labels to a multi-class dataset:

This way, standard multi-class algorithms can be used to predict the multi-class label and consequently the original target labels. However, the Label-PowerSet method has a few shortcomings:

• Complexity: the more target labels, the bigger the solution space (up to 2^L combinations);
• Imbalance (label sparsity) and Overfitting: because of the size of the solution space, the dataset is very imbalanced since only a few examples actually target some class labels.

Possible solutions involve pruning, or dealing with subsets.

Solution 4 - Meta Labels

Another approach is to combine the advantages of the Label-PowerSet method and Binary Relevance. A Meta-Labels approach is made of 3 operations:

• decomposing the labelset into M sets of k labels, , e.g., {A, B}∪{C} or {A, B}∪{B,C}
• relabeling applying Label-PowerSet method
• recombination of meta-label prediction into a label vector prediction

This approach greatly reduces the problem complexity, from O(2^L) to O(M*2^k), by reducing the size of Y (the number of combinations), it reduces connectivity among the target label y_i, and it even increases predictive performance.

Multi-Label Evaluation

In single-label classification problem, computing the accuracy of a model is pretty easy, since one should only compare whether the prediction is equal to the target label or not (emitting 1 if this condition holds, 0 otherwise), and then computing the mean of this values.

However, in multi-label classification, comparing a prediction with the target would look something like this:

This calls for a new definition of evaluation metrics, since other condition apart from equality might give further insight into the problem. Given the target solution and our prediction, it is possible to compute:

• Hamming Loss:
  • It basically represents how many bits are different globally
  • in the example, 4 bits are different from the left set (target set) = 4/20 = 1/5 = 20%
• 0/1 Loss:
  • How many predictions present at least a bit different from the target set
  • in the example, 3 solutions are different out of 5 = 3/5 = 0.60
  • often used as Exact Match, by computing 1-0/1Loss
• Accuracy/Jaccard Index:
  • average of the [count 1 of the AND between target and prediction] / [count 1 of the OR between target and prediction] (each number is at most 1!)
  • in the example:
    • AND(1010, 1001) = 1 | OR(1010, 1001) = 3 --> 1/3
    • apply for all of them
    • 1/5 (1/3 + 1 + 1 + 1/2 + 1/2) = 0.67

However, sometimes it might be useful to evaluate probabilities/confidences rather than predictions:

In this case, we want to evaluate Log Loss, like Hamming Loss, to encourage "good confidence". The goal of our machine learning models is to minimize this value. A perfect model would have a log loss of 0. Log loss increases as the predicted probability diverges from the actual label. Log Loss takes into account the uncertainty of your prediction based on how much it varies from the actual label. This gives us a more nuanced view into the performance of our model. Instead, accuracy is the count of predictions where your predicted value equals the actual value. Accuracy is not always a good indicator because of its yes or no nature. The log loss function is also known as cross entropy for multiple classes.

Let's suppose, given the dataset above, that we want to compute the logarithmic loss for when the third bit is 1. So, for y[2] = 1, we predict wrong twice, with 0.4 and 0.1 probability. Therefore, we have two losses: -log(0.4) = 0.92 and -log(0.1) = 2.30

Sometimes, even ranking can be used as a metric, in order to evaluate the average fraction of label pairs mis-ordered for x: we compute the Ranking Loss, as where . The function I(c) return true if the condition c holds.

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Ensemble Learning

In machine learning classification, an ensemble of classifiers is a collection of several models combined together, in a form which can be generalized as the following algorithm:

This procedure requires three elements to create an ensemble:

1. A set S of training examples
2. A base learning algorithm
3. A method of assigning weights to examples (line 3 of the pseudo-code)

The third requirement, the weighting of examples, forms the major difference between ensemble methods. Another potential difference is the voting procedure. Typically each member of the ensemble votes towards the prediction of class labels, where voting is either weighted or unweighted. In weighted voting individual classifiers have varying influence on the final combined vote, the models that are believed to be more accurate will be trusted more than those that are less accurate on average. In unweighted voting all models have equal weight, and the final predicted class is the label chosen by the majority of ensemble members. Ensemble algorithms, algorithms responsible for inducing an ensemble of models, are sometimes known as meta-learning schemes. They perform a higher level of learning that relies on lower-level base methods to produce the individual models.

Ensemble: Bagging

Bagging (bootstrap aggregating) combines the unweighted vote of multiple classifiers, each of which is trained on a different bootstrap replicate of the training set. A bootstrap replicate is a set of examples drawn randomly with replacement from the original training data, to match the size of the original training data. Bagging builds a set of M base models, with a bootstrap sample created by drawing random samples with replacement.

This algorithm does not seem immediately applicable to data streams, because it appears that the entire data set is needed in order to construct bootstrap replicates. However, a modified version for incremental learning has been proposed by Oza and Russell, called Online Bagging. They demonstrated that the process of sampling bootstrap replicates from training data follows a Binomial Distribution, which tends to a Poisson(1) distribution with N->infinite. In their algorithm, they use this distribution to decide how many times to include the incoming example in the formation of a replicate set. This online bagging algorithm converges towards the original batch algorithm, which required the full dataset to be known.

Example:

A1	A2	A3
.1	.2	+
.3	.8	-
.4	.3	+
.3	.2	-
.5	.7	+

Given a Poisson Distribution P(1) = {0,1,0,1,0,3,2,2,0,0,0,1,1,1,1}, build an Online Bagging majority vote classifier made of 3 majority vote classifier.

The Poisson(1) distribution is used to give weights to each example, for every model. The algorithm is:

0	i = 0
1 	for each example
2		for each model
3			emit(<a3,weight=P(1)[i]>)
4	majority vote for each classifier
5 	majority vote across classifier

STEP 1, example 0: <.1,.2,+>

• m1, w = P(1)[0] = 0 => m1(<+,0>)
• m2, w = P(1)[1] = 1 => m2(<+,1>)
• m3, w = P(1)[2] = 0 => m3(<+,0>)

STEP 2, example 1: <.3,.8,->

• m1, w = P(1)[3] = 1 => m1(<-,1>)
• m2, w = P(1)[4] = 0 => m2(<-,0>)
• m3, w = P(1)[5] = 3 => m3(<-,3>)

STEP 3, example 2: <.4,.3,+>

• m1, w = P(1)[6] = 2 => m1(<+,2>)
• m2, w = P(1)[7] = 2 => m2(<+,2>)
• m3, w = P(1)[8] = 0 => m3(<+,0>)

STEP 4, example 3: <.3,.2,->

• m1, w = P(1)[9] = 0 => m1(<-,0>)
• m2, w = P(1)[10] = 0 => m2(<-,0>)
• m3, w = P(1)[11] = 1 => m3(<-,1>)

STEP 5, example 4: <.5,.7,+>

• m1, w = P(1)[12] = 1 => m1(<+,1>)
• m2, w = P(1)[14] = 1 => m2(<+,1>)
• m3, w = P(1)[13] = 1 => m3(<+,1>)

CLASSIFIER M1

• <+,0> ; <-,1> ; <+,2> ; <-,0> ; <+,1> => -1+2+1=2 => emit(+)

CLASSIFIER M2

• <+,1> ; <-,0> ; <+,2> ; <-,0> ; <+,1> => +1+2=3 => emit(+)

CLASSIFIER M3

• <+,0> ; <-,3> ; <+,0> ; <-,1> ; <+,1> => -3-1+1 => emit(-)

ENSEMBLE

• <+,+,-> => emit(+)

Ensemble: Hoeffding Option Trees

The Hoeffding option tree is a regular Hoeffding tree containing additional option nodes that allow several tests to be applied, leading to multiple Hoeffding trees as separate paths. The Hoeffding option tree uses the class confidences in each leaf to help form the majority decision.

Ensemble: Accuracy Weighted Ensemble

The accuracy weighted ensemble algorithm consists of processing chunks of instances with size W by building a new classifier for each of the chunks (removing the old one). Then, each classifiier is weighted using the difference of Mean Square Error for the newly built model and the current one

For two classes classification with equal class priors, MSE_r = 0.25. Classifiers with accuracy less or equal than random classifier will be assigned weight 0 and the weights of other classifiers will be inversely proportional to their error in classifying calibration data of current user.

Ensemble: Random Forests

Random Forests is a method to use randomization on the input and on the internal construction of the decision trees. Random Forests are ensembles of trees with the following characteristics: the input training set is obtained by sampling with replacement, the nodes of the tree only may use a fixed number of random attributes to split, and the trees are grown without pruning.

Ensemble: ADWIN Bagging

When a change is detected, the worst classifier is removed and a new classifier is added.

Ensemble: Leveraging Bagging

The performance of bagging algorithms can be improved, in terms of accuracy and diversity, by means of three possible randomization:

• manipulating the input data
• manipulating the classifier algorithms
• manipulating the output targets

Where the classic bagging algorithm, proposed previously, uses a Poisson(λ), λ = 1 distribution, it is possible to use greater values of λ: this increases the diversity of the weights and modifies the input space of the classifiers inside the ensemble. However, the optimal value of λ may be different for each dataset. This is the Leveraging Bagging algorithm.

Another further improvement is to add randomization at the output of the ensemble using output codes: this is the Leveraging Bagging MC algorithm, which combines Poisson(λ) distribution and Random Output Codes. This algorithm assigns for each classifier m and class c a binary value µ_m(c) in a uniform, independent, and random way, but ensuring that half of the classes are mapped to 0. The output of the classifier for an example is the class which has more votes of its binary mapping classes. This is great for solving multi-class problems with binary classifiers.

The Fast Leveraging Bagging ME (misclassified examples) goes one step beyond that. It gives a weight = 1 to misclassified example: if an instance is misclassified it is accepted with a weight of one. If not, it is accepted with probability e_T /(1 − e_T ), where the error estimate e_T is computed as a smoothed version of the proportion of misclassified examples using the estimation of ADWIN that is monitoring the error. This makes the algorithm very fast, although not much more accurate.

An empirical evaluation is the following:

Ensemble: Boosting

For batch learning, a boosting algorithm was defined as an algorithm that transforms a weak learner into a strong one. An example is AdaBoost, which sequentially constructs a series of base learner in such a way that examples that are misclassified by current base learner h_m are given more weights in the training set for the following learner h_m+1, whereas the correctly classified examples are given less weights. It can be said that AdaBoost focuses on difficult examples.

Oza and Russell proposed their version of Online Boosting algorithm.

When a base model h_m misclassifies a training example, the Poisson distribution parameter λ associated with that example is increased when presented to the next base model; otherwise, it is decreased.

Ensemble: Stacking