ROSE: Robust Online Self-Adjusting Ensemble for Continual Learning from Imbalanced Drifting Data Streams

Data streams are potentially unbounded sequences of instances arriving over time to a classifier. Designing algorithms that are capable of dealing with massive, rapidly arriving information is one of the most dynamically developing areas of machine learning. Such learners must be able to deal with a phenomenon known as concept drift, where the data stream may be subject to various changes in its characteristics over time. Furthermore, distributions of classes may evolve over time, leading to a highly difficult non-stationary class imbalance. In this work we introduce Robust Online Self-Adjusting Ensemble (ROSE), a novel online ensemble classifier capable of dealing with all of the mentioned challenges. The main features of ROSE are: (i) online training of base classifiers on variable size random subsets of features; (ii) online detection of concept drift and creation of a background ensemble for faster adaptation to changes; (iii) sliding window per class to create skew-insensitive classifiers regardless of the current imbalance ratio; and (iv) self-adjusting bagging to enhance the exposure of difficult instances from minority classes. The interplay among these features leads to an improved performance in various data stream mining benchmarks. An extensive experimental study comparing with 30 ensemble classifiers shows that ROSE is a robust and well-rounded classifier for drifting imbalanced data streams, especially under the presence of noise and class imbalance drift, while maintaining competitive time complexity and memory consumption. Results are supported by a thorough non-parametric statistical analysis.

Using ROSE

Download the pre-compiled jar files or import the project source code into MOA. See the src/main/java/experiments folder to reproduce our research.

Experiment 1: Static imbalance ratio

Use any algorithm in moa.classifiers and imbalanced generator in moa.streams.generators.imbalanced. The parameter -m controls the proportion of the minority vs majority class, e.g. -m 0.01 reflects an imbalance ratio of 100.

java -javaagent:sizeofag-1.0.4.jar -cp ROSE-1.0.jar:MOA-dependencies.jar moa.DoTask EvaluateInterleavedTestThenTrain -e "(WindowAUCImbalancedPerformanceEvaluator)" -s "(moa.streams.generators.imbalanced.AgrawalGenerator -i 1 -f 1 -m 0.01)" -l "(moa.classifiers.meta.imbalanced.ROSE)" -i 1000000 -f 500 -d results.csv

Generator	Instances	Features	Classes	Static Imbalance Ratios	Concept Drift
Agrawal	1,000,000	9	2	{5, 10, 20, 50, 100}	None
AssetNegotiation	1,000,000	5	2	{5, 10, 20, 50, 100}	None
RandomRBF	1,000,000	10	2	{5, 10, 20, 50, 100}	None
SEA	1,000,000	3	2	{5, 10, 20, 50, 100}	None
Sine	1,000,000	4	2	{5, 10, 20, 50, 100}	None
Hyperplane	1,000,000	10	2	{5, 10, 20, 50, 100}	None

Experiment 2: Drifting imbalance ratio

Use any algorithm in moa.classifiers and imbalanced generator in moa.streams.generators.imbalanced. The parameter -m controls the proportion of the minority vs majority class, e.g. -m 0.01 reflects an imbalance ratio of 100. Generate drifting imbalance ratios by chaining ConceptDriftStream streams with different imbalance ratios. The parameter -p controls the position of the drift and -w the width of the drift (sudden vs gradual). The example shows a sequence of increasing then decreasing imbalance ratio ({5, 10, 20, 100, 20, 10, 5}).

java -javaagent:sizeofag-1.0.4.jar -cp ROSE-1.0.jar:MOA-dependencies.jar moa.DoTask EvaluateInterleavedTestThenTrain -e "(WindowAUCImbalancedPerformanceEvaluator)" -s "(ConceptDriftStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 1 -f 2 -m 0.2) -r 1 -d (ConceptDriftStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 2 -f 2 -m 0.1) -r 2 -d (ConceptDriftStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 3 -f 2 -m 0.05) -r 3 -d (ConceptDriftStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 4 -f 2 -m 0.01) -r 4 -d (ConceptDriftStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 5 -f 2 -m 0.01) -r 5 -d (ConceptDriftStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 6 -f 2 -m 0.05) -r 6 -d (ConceptDriftStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 7 -f 2 -m 0.1) -r 7 -d (moa.streams.generators.imbalanced.AgrawalGenerator -i 8 -f 2 -m 0.2) -r 8 -p 125000 -w 1) -p 125000 -w 1) -p 125000 -w 1) -p 125000 -w 1) -p 125000 -w 1) -p 125000 -w 1) -p 125000 -w 1)" -l "(moa.classifiers.meta.imbalanced.ROSE)" -i 1000000 -f 500 -d results.csv

Generator	Instances	Features	Classes	Drifting imbalance ratios	Concept Drift
Agrawal	1,000,000	9	2	{5, 10, 20, 100, 20, 10, 5}	8 drifts {sudden, gradual}
AssetNegotiation	1,000,000	5	2	{5, 10, 20, 100, 20, 10, 5}	8 drifts {sudden, gradual}
RandomRBF	1,000,000	10	2	{5, 10, 20, 100, 20, 10, 5}	8 drifts {sudden, gradual}
SEA	1,000,000	3	2	{5, 10, 20, 100, 20, 10, 5}	8 drifts {sudden, gradual}
Sine	1,000,000	4	2	{5, 10, 20, 100, 20, 10, 5}	8 drifts {sudden, gradual}
Hyperplane	1,000,000	10	2	{5, 10, 20, 100, 20, 10, 5}	8 drifts {sudden, gradual}

Experiment 3: Instance-level difficulties

Use any algorithm in moa.classifiers and dataset for instance-level difficulties generated using these imbalanced generators

java -javaagent:sizeofag-1.0.4.jar -cp ROSE-1.0.jar:MOA-dependencies.jar moa.DoTask EvaluateInterleavedTestThenTrain -e "(WindowAUCImbalancedPerformanceEvaluator)" -s "(ArffFileStream -f Split5+Im1+Borderline20+Rare20.arff)" -l "(moa.classifiers.meta.OSAKUE_20200935_t99_scaleacckappa)" -f 500 -d results.csv

Generator	Instances	Features	Classes	Static Imbalance Ratios	Percentage of difficult instances
Borderline	200,000	5	2	{1, 10, 100}	{20%, 40%, 60%, 80%, 100%}
Rare	200,000	5	2	{1, 10, 100}	{20%, 40%, 60%, 80%, 100%}
Borderline + Rare	200,000	5	2	{1, 10, 100}	{20%, 40%}

Experiment 4: Robustness to noise drift

Use any algorithm in moa.classifiers and imbalanced generator in moa.streams.generators.imbalanced. The parameter -f controls the percentage of features with noise. The parameter -m controls the proportion of the minority vs majority class, e.g. -m 0.01 reflects an imbalance ratio of 100. Generate drifting noise and imbalance ratios by chaining ConceptDriftStream streams with different imbalance ratios, percentages of features with noise, and noise seed -r. The parameter -p controls the position of the drift and -w the width of the drift (sudden vs gradual). The example shows a sequence of drifting noise to other features and increasing then decreasing imbalance ratio ({5, 10, 20, 100, 20, 10, 5}).

java -javaagent:sizeofag-1.0.4.jar -cp ROSE-1.0.jar:MOA-dependencies.jar moa.DoTask EvaluateInterleavedTestThenTrain -e "(WindowAUCImbalancedPerformanceEvaluator)" -s "(ConceptDriftStream -s (FilteredStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 1 -f 2 -m 0.2) -f (AddNoiseFilterFeatures -r 1 -a 0.99 -f 0.40)) -r 1 -d (ConceptDriftStream -s (FilteredStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 2 -f 2 -m 0.1) -f (AddNoiseFilterFeatures -r 2 -a 0.99 -f 0.40)) -r 2 -d (ConceptDriftStream -s (FilteredStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 3 -f 2 -m 0.05) -f (AddNoiseFilterFeatures -r 3 -a 0.99 -f 0.40)) -r 3 -d (ConceptDriftStream -s (FilteredStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 4 -f 2 -m 0.01) -f (AddNoiseFilterFeatures -r 4 -a 0.99 -f 0.40)) -r 4 -d (ConceptDriftStream -s (FilteredStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 5 -f 2 -m 0.01) -f (AddNoiseFilterFeatures -r 5 -a 0.99 -f 0.40)) -r 5 -d (ConceptDriftStream -s (FilteredStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 6 -f 2 -m 0.05) -f (AddNoiseFilterFeatures -r 6 -a 0.99 -f 0.40)) -r 6 -d (ConceptDriftStream -s (FilteredStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 7 -f 2 -m 0.1) -f (AddNoiseFilterFeatures -r 7 -a 0.99 -f 0.40)) -r 7 -d (FilteredStream -s (moa.streams.generators.imbalanced.AgrawalGenerator -i 8 -f 2 -m 0.2) -f (AddNoiseFilterFeatures -r 8 -a 0.99 -f 0.40)) -r 8 -p 125000 -w 1) -p 125000 -w 1) -p 125000 -w 1) -p 125000 -w 1) -p 125000 -w 1) -p 125000 -w 1) -p 125000 -w 1)" -l "(moa.classifiers.meta.imbalanced.ROSE)" -i 1000000 -f 500 -d results.csv

Generator	Instances	Features	Classes	Drifting imbalance ratios	Concept Drift	Percentage of features with noise
Agrawal	1,000,000	9	2	{5, 10, 20, 100, 20, 10, 5}	8 drifts {sudden, gradual}	{10%, 20%, 30%, 40%}
AssetNegotiation	1,000,000	5	2	{5, 10, 20, 100, 20, 10, 5}	8 drifts {sudden, gradual}	{10%, 20%, 30%, 40%}
RandomRBF	1,000,000	10	2	{5, 10, 20, 100, 20, 10, 5}	8 drifts {sudden, gradual}	{10%, 20%, 30%, 40%}
SEA	1,000,000	3	2	{5, 10, 20, 100, 20, 10, 5}	8 drifts {sudden, gradual}	{10%, 20%, 30%, 40%}
Sine	1,000,000	4	2	{5, 10, 20, 100, 20, 10, 5}	8 drifts {sudden, gradual}	{10%, 20%, 30%, 40%}
Hyperplane	1,000,000	10	2	{5, 10, 20, 100, 20, 10, 5}	8 drifts {sudden, gradual}	{10%, 20%, 30%, 40%}

Experiment 5: Datasets

Use any algorithm in moa.classifiers and dataset from UCI / KEEL dataset repositories.

java -javaagent:sizeofag-1.0.4.jar -cp ROSE-1.0.jar:MOA-dependencies.jar moa.DoTask EvaluateInterleavedTestThenTrain -e "(WindowAUCMultiClassImbalancedPerformanceEvaluator)" -s "(ArffFileStream -f dataset.arff)" -l "(moa.classifiers.meta.imbalanced.ROSE)" -f 500 -d results.csv

Dataset	Instances	Features	Classes
adult	45,222	14	2
airlines	539,383	7	2
bridges	1,000,000	12	6
census	299,284	41	2
coil2000	9,822	85	2
connect-4	67,557	42	3
covtype	581,012	54	7
dj30	138,166	7	30
electricity	45,312	8	2
fars	100,968	29	8
gas-sensor	13,910	128	6
gmsc	150,000	10	2
intel-lab	2,313,153	5	58
kddcup	4,898,431	41	23
kr-vs-k	28,056	6	18
letter	20,000	16	26
magic	19,020	10	2
nomao	34,465	118	2
penbased	10,992	16	10
poker	829,201	10	10
powersupply	29,928	2	24
shuttle	57,999	9	7
thyroid	7,200	21	3
zoo	1,000,000	17	7

Citation

@article{cano2022rose,
  title={{ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams}},
  author={Cano, Alberto and Krawczyk, Bartosz},
  journal={Machine Learning},
  volume={111},
  number={7},
  pages={2561--2599},
  year={2022}
}