Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6904058 | Applied Soft Computing | 2018 | 41 Pages |
Abstract
Master River Multiple Creeks Intelligent Water Drops (MRMC-IWD) is an ensemble model of the intelligent water drop, whereby a divide-and-conquer strategy is utilized to improve the search process. In this paper, the potential of the MRMC-IWD using real-world optimization problems related to feature selection and classification tasks is assessed. An experimental study on a number of publicly available benchmark data sets and two real-world problems, namely human motion detection and motor fault detection, are conducted. Comparative studies pertaining to the features reduction and classification accuracies using different evaluation techniques (consistency-based, CFS, and FRFS) and classifiers (i.e., C4.5, VQNN, and SVM) are conducted. The results ascertain the effectiveness of the MRMC-IWD in improving the performance of the original IWD algorithm as well as undertaking real-world optimization problems.
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Authors
Basem O. Alijla, Chee Peng Lim, Li-Pei Wong, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar,