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Improved multi-class classification approach for imbalanced big data on spark

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Abstract

The difficulty in classifying imbalanced datasets is one of the complexities preventing meaningful information from being extracted. It is common in the actual data applications for instances from a class of primary concern to be overshadowed by instances from other classes. The majority of known methods are designed for binary imbalanced classification with well-defined minority and majority classes. Additionally, in the frameworks that process the data in a distributed manner like Apache Spark, data partitioning is not controlled, which leads to chunks of data with corrupted spatial relationships. In this study, we propose an efficient method for dealing with the multi-class classification of imbalanced large datasets in Apache Spark. It utilizes Random Sample Partitioning to ensure the effective distribution of the dataset among different partitions. Subsequently, block-level sampling deals with efficient data sampling. This method overcomes the spatial limitations of distributed environments by utilizing an improved version of the synthetic minority oversampling technique (SMOTE). The synthetic data point generation through improved SMOTE assures a sufficient number of instances for the minority classes against the entire dataset. Extensive experiments were carried out on Apache Spark Cluster using the Random Forest and Naive Bayes algorithms on the four benchmarks imbalanced datasets. With relatively limited data samples, the proposed methodology can effectively classify unknown data samples from large datasets.

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Data availability

The datasets analyzed during the current study are available at: a. https://archive.ics.uci.edu/ml/datasets/Covertype. b. https://archive.ics.uci.edu/ml/datasets/detection_of_IoT_botnet_attacks_N_BaIoT. c. https://www.seer.cancer.gov. d. https://catalog.data.gov/dataset/traffic-violations-56dda.

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Author notes

  1. Riya Khanna, Satakshi and Manish Kumar have contributed equally to this work.

Authors and Affiliations

  1. Department of IT, Indian Institute of Information Technology Allahabad, Prayagraj, U.P., India

    Tinku Singh, Riya Khanna & Manish Kumar

  2. Department of Mathematics & Statistics, SHUATS, Prayagraj, U.P., India

    Satakshi

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Singh, T., Khanna, R., Satakshi et al. Improved multi-class classification approach for imbalanced big data on spark. J Supercomput 79, 6583–6611 (2023). https://doi.org/10.1007/s11227-022-04908-3

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