M. Priyadharsini1,*, K. Karuppasamy2
Computer Systems Science and Engineering, Vol.45, No.2, pp. 2187-2205, 2023, DOI:10.32604/csse.2023.031115
Abstract Big Data applications face different types of complexities in classifications. Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data. The existing scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation. When comparing to a single model, this technique offers for improved prediction. Ensemble based feature selections parallel multiple expert’s judgments on a… More >
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