pitfalls in benchmarking data stream classification and how to avoid them

Pitfalls in Benchmarking Data StreamClassification and How to Avoid Them

Albert Bifet1, Jesse Read2, Indre Zliobaite3

Bernhard Pfahringer4, Geoff Holmes4

1Yahoo! Research Barcelona2Universidad Carlos III, Madrid, Spain

3Aalto University and Helsinki Institute for Information Technology (HIIT), Finland4University of Waikato, Hamilton, New Zealand

ECML-PKDD 2013, 25 September 2013

Data Streams

Data StreamsI Sequence is potentially infiniteI High amount of data: sublinear spaceI High speed of arrival: sublinear time per exampleI Once an element from a data stream has been processed

it is discarded or archived

Big Data & Real Time

1. Motivation

Electricity Dataset

I Popular benchmark for testing adaptive classifiersI Collected from the Australian New South Wales Electricity

Market.I Contains 45,312 instances which record electricity prices

at 30 minute intervals.I The class label identifies the change of the price (UP or

DOWN) related to a moving average of the last 24 hours.

Electricity Dataset, Accuracy

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VFDT Majority ClassNaive Bayes

Electricity Dataset, Accuracy

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Magic Classifier VFDTMajority Class Naive Bayes

Electricity Dataset, Kappa Statistic

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Electricity Dataset, Kappa Statistic

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Magic Classifier VFDTNaive Bayes

Electricity Dataset, Accuracy

Algorithm name Acc. (%) Algorithm name Acc. (%)DDM 89.6* Local detection 80.4Learn++.CDS 88.5 Perceptron 79.1KNN-SPRT 88.0 AUE2 77.3GRI 88.0 ADWIN 76.6FISH3 86.2 EAE 76.6EDDM-IB1 85.7 Prop. method 76.1Magic classifier 85.3 Cont. λ-perc. 74.1ASHT 84.8 CALDS 72.5bagADWIN 82.8 TA-SVM 68.9DWM-NB 80.8* tested on a subset

2. Problem

No-Change classifier: Weather classifier

Prediction for tomorrow: the same astoday

Electricity Dataset, Accuracy

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No-Change VFDTMajority Class Naive Bayes

Electricity Dataset, Kappa Statistic

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Characteristics of the Electricity Dataset

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Characteristics of the Electricity Dataset

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3. Proposal

New Evaluation for Stream Classifiers

Kappa Statistic

I p0: classifier’s prequential accuracyI pc : probability that a chance classifier makes a correct

prediction.I κ statistic

κ =p0 − pc

1 − pc

I κ = 1 if the classifier is always correctI κ = 0 if the predictions coincide with the correct ones as

often as those of the chance classifier

New Evaluation for Stream Classifiers

Kappa Plus Statistic

I p0: classifier’s prequential accuracyI pe: no-change classifier’s prequential accuracy

I κ+ statisticκ+ =

p0 − pe

1 − pe

I κ+ = 1 if the classifier is always correctI κ+ = 0 if the predictions coincide with the correct ones as

often as those of the no-change classifier

Electricity Market Dataset Accuracy

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No-Change HATLev. Bagging

Electricity Market Dataset κ

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Electricity Market Dataset κ+

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SWT: Temporally Augmented Classifier

SWT: meta strategy that builds meta instances by augmentingthe original input attributes with the values of recent classlabels from the past

Pr [class is c] ≡ h(x t , ct−`, . . . , ct−1)

for the t-th test instance, where ` is the size of the slidingwindow over the most recent true labels.

Electricity Market Dataset κ+

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No-Change SWT HATSWT Lev. Bagging

Electricity Market Dataset κ+

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Electricity Market Dataset κ+

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No-Change SWT HATSWT Lev. Bagging

Forest Cover Type Dataset

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No-Change HATLev. Bagging

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No-Change SWT HATSWT Lev. Bagging

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No-Change SWT HATSWT Lev. Bagging

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Conclusions

Temporal dependence in data stream mining

I new κ+ measureI a wrapper classifier SWT

Pitfalls in Benchmarking Data StreamClassification and How to Avoid Them

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Pitfalls in Benchmarking Data StreamClassification and How to Avoid Them