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.
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.
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 |
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} |
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%} |
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%} |
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 |
@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}
}