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An ensemble of intelligent water drop algorithm for feature selection optimization problem
B.O. Alijla, C.P. Lim, L.-P. Wong, A.T. Khader,
Published in Elsevier Ltd
Volume: 65
Pages: 531 - 541
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. © 2018 Elsevier B.V.
About the journal
JournalData powered by TypesetApplied Soft Computing Journal
PublisherData powered by TypesetElsevier Ltd
Open AccessNo