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The Fuzzy Min-Max Neural Network (FMNN) Classification algorithm gained prominence due to its distinct characteristics, such as adaptability for incremental learning, nonlinear classification, and single-pass training. There were many extensions to FMNN since it was incepted. Among them, a couple of works have been done to improve the scalability of FMNN. The MapReduce framework has been widely employed in recent years to scale machine learning algorithms. The existing MapReduce-based FMNN (MRCFMNN) that uses centroid for classification was found to have limitations regarding generalizability due to outliers. This work proposes medoid-based algorithms MRMFMNN_1 and MRMFMNN_2 to overcome the limitations associated with MRCFMNN. The Apache Spark cluster was used to perform the comparative experimental study between the proposed approaches MRMFMNN_1, MRMFMNN_2, and existing state-of-the-art approaches MRCFMNN, MRFMNN using benchmark large decision systems. The experimental outcomes empirically determine the supremacy of the proposed MRMFMNN_1 algorithm by accomplishing improved generalizability over existing MRCFMNN and MRFMNN.

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Scalable MapReduce-Based Fuzzy Min-Max Neural Network Using KNN-Medoids for Pattern Classification

  • 会议论文

  • 发表日期：

    2026年

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  • Vadlamudi Aadarsh,
  • Pati Sai Praneeth,
  • P. S. V. S. Sai Prasad

摘要

The Fuzzy Min-Max Neural Network (FMNN) Classification algorithm gained prominence due to its distinct characteristics, such as adaptability for incremental learning, nonlinear classification, and single-pass training. There were many extensions to FMNN since it was incepted. Among them, a couple of works have been done to improve the scalability of FMNN. The MapReduce framework has been widely employed in recent years to scale machine learning algorithms. The existing MapReduce-based FMNN (MRCFMNN) that uses centroid for classification was found to have limitations regarding generalizability due to outliers. This work proposes medoid-based algorithms MRMFMNN_1 and MRMFMNN_2 to overcome the limitations associated with MRCFMNN. The Apache Spark cluster was used to perform the comparative experimental study between the proposed approaches MRMFMNN_1, MRMFMNN_2, and existing state-of-the-art approaches MRCFMNN, MRFMNN using benchmark large decision systems. The experimental outcomes empirically determine the supremacy of the proposed MRMFMNN_1 algorithm by accomplishing improved generalizability over existing MRCFMNN and MRFMNN.