An active learning budget-based oversampling approach for partially labeled multi-class imbalanced data streams
This repository provides the source code, algorithms, experimental setup, and results for the budget-based overamspling method paper presented at the 38th ACM/SIGAPP Symposium On Applied Computing. The manuscript is available at paper.
Learning classification models from multi-class imbalanced data streams is a challenging task in machine learning. Moreover, there is a common assumption that all instances are labeled and available for the training phase. However, this is not realistic in real-world scenarios when learning from partially labeled data. In this work, we propose an active learning method based on labeling budget that can tackle multi-class imbalance data, concept drift, and limited access to labels. The proposed method combines information from budget constraints and dynamic class ratios to generate new relevant instances. We performed experiments on 18 real-world data streams and 11 semi-synthetic data streams, under different labeling budgets, in order to evaluate the performance of the proposed method under a varied set of scenarios. The experimental study showed that our oversampling method was able to improve the performance of stateof-the-art classifiers for multi-class imbalanced data streams under strict budgets and outperforms previously proposed oversampling methods in the domain.
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. We use the MOA framework and its class hierarchy. Adding a new algorithm, generator, or evaluator is the same as adding it in MOA (see MOA documentation).
Download the pre-compiled budgetbasedoversampling-1.0-jar-with-dependencies.jar or import the project source code into MOA. Download the multi-class datasets.
java -javaagent:sizeofag-1.0.4.jar -cp budgetbasedoversampling-1.0-jar-with-dependencies.jar moa.DoTask moa.tasks.meta.ALPrequentialEvaluationTask -e "(ALMultiClassImbalancedPerformanceEvaluator -w 500)" -s "(ArffFileStream -f datasets/semi-synth/CRIMES-D1.arff)" -l "(moa.classifiers.active.ALRandom -l (moa.classifiers.meta.imbalanced.BudgetImbalancedOversampling -l moa.classifiers.trees.HDVFDT)" -b "(moa.classifiers.active.budget.FixedBM -b 0.1))" -f 500 -d results.csv
The package src/main/java/experiments/active provides the scripts for the experiments experiments. Use any of the scripts provided at src/main/java/experiments/active for the different groups of experiments and add your algorithm, generator, or evaluator. These scripts will generate the command lines used to run the experiments
@inproceedings{aguiar2023active,
title={An active learning budget-based oversampling approach for partially labeled multi-class imbalanced data streams},
author={Aguiar, Gabriel J and Cano, Alberto},
booktitle={38th ACM/SIGAPP Symposium On Applied Computing},
year={2023}
}