Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach

High dimension data are often associated with redundant features and there exist many information-theoretic approaches used to select the most relevant set of features and to reduce the feature size. The three most significant approaches are filter, wrap- per, and embedded approaches. Most filter ap...

全面介绍

Saved in:
书目详细资料
主要作者: Jothi, Neesha
格式: Thesis
语言:English
出版: 2020
主题:
在线阅读:http://eprints.usm.my/52445/1/Pages%20from%202.%20Final%20Thesis%20Submission.pdf
标签: 添加标签
没有标签, 成为第一个标记此记录!
实物特征
总结:High dimension data are often associated with redundant features and there exist many information-theoretic approaches used to select the most relevant set of features and to reduce the feature size. The three most significant approaches are filter, wrap- per, and embedded approaches. Most filter approaches fail to identify the individual contribution of every feature in each set of features in achieving an optimal feature subset. While the wrapper approaches encounter problems from complex interactions among features and stagnation in local optima. To address, these drawbacks, this study investigates filter and wrapper approaches to develop effective hybrid approaches for feature selection.