Abstract
Fuzzy Min-Max Neural Network (FMNN) Classifier has acquired significance owing to its unique properties of single-pass training, non-linear classification, and adaptability for incremental learning. Since its inception in 1992, FMNN has witnessed several extensions, modifications, and utilization in various applications. But very few works are done in the literature for enhancing the scalability of FMNN. In recent years, MapReduce framework is used extensively for scaling machine learning algorithms. The existing MapReduce approach for FMNN (MRFMNN) is found to be having limitations in load balancing and in achieving good generalizability. This work proposes MRCFMNN Algorithm for overcoming these limitations. MRCFMNN induces an ensemble of centroid-based FMNN Classifiers for achieving higher generalizability with load balancing. Four ensemble strategies are proposed for combining the individual classifier results. The comparative experimental results using benchmark large decision systems were conducted on Apache Spark MapReduce cluster. The results empirically establish the relevance of the proposed MRCFMNN by achieving significantly better classification accuracy in most of the datasets over MRFMNN.
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References
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)
Ilager, S., Prasad, P.S.: Scalable mapreduce-based fuzzy min-max neural network for pattern classification. In: Proceedings of the 18th International Conference on Distributed Computing and Networking, pp. 1–7 (2017)
Kumar, A., Prasad, P.S.V.S.S.: Scalable fuzzy rough set reduct computation using fuzzy min-max neural network preprocessing. IEEE Trans. Fuzzy Syst. 28(5), 953–964 (2020). https://doi.org/10.1109/TFUZZ.2020.2965899
Liu, J., Ma, Y., Zhang, H., Su, H., **ao, G.: A modified fuzzy min-max neural network for data clustering and its application on pipeline internal inspection data. Neurocomputing 238, 56–66 (2017)
Mohammed, M.F., Lim, C.P.: An enhanced fuzzy min-max neural network for pattern classification. IEEE Trans. Neural Netw. Learn. Syst. 26(3), 417–429 (2014)
Nandedkar, A.V., Biswas, P.K.: A fuzzy min-max neural network classifier with compensatory neuron architecture. IEEE Trans. Neural Netw. 18(1), 42–54 (2007)
Quteishat, A., Lim, C.P.: A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification. Appl. Soft Comput. 8(2), 985–995 (2008)
Simpson, P.K.: Fuzzy min–max neural networks–part 1: classification. IEEE Trans. Neural Netw. 3(5), 776–786 (1992)
Spark, A.: Apache spark: lightning-fast cluster computing, pp. 2168–7161 (2016). http://spark.apache.org
Zhang, H., Liu, J., Ma, D., Wang, Z.: Data-core-based fuzzy min-max neural network for pattern classification. IEEE Trans. Neural Netw. 22(12), 2339–2352 (2011)
Dua, D., Graff, C.: UCI Machine Learning Repository (Technical report, University of California, Irvine, School of Information and Computer Sciences) (2017)
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Aadarsh, V., Prasad, P.S.V.S.S. (2023). Scalable Centroid Based Fuzzy Min-Max Neural Network Ensemble Classifier Using MapReduce. In: Maji, P., Huang, T., Pal, N.R., Chaudhury, S., De, R.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2023. Lecture Notes in Computer Science, vol 14301. Springer, Cham. https://doi.org/10.1007/978-3-031-45170-6_62
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DOI: https://doi.org/10.1007/978-3-031-45170-6_62
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