Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12358/25157
Title | An ensemble of intelligent water drop algorithm for feature selection optimization problem |
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Untitled | |
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. |
Type | Journal Article |
Date | 2018 |
Published in | Applied Soft Computing |
Series | Volume: 65 |
Publisher | Elsevier |
Citation | |
Item link | Item Link |
License | ![]() |
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Files in this item | ||
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1-s2.0-S1568494618300528-main.pdf | 1.095Mb |