期刊
PATTERN RECOGNITION
卷 67, 期 -, 页码 47-61出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2017.01.026
关键词
Dimensionality reduction; Feature selection; Classification; Correlation measure; Qualitative and quantitative variables
资金
- Ministry of Higher Education Malaysia
- Engineering and Physical Sciences Research Council (EPSRC) [EP/I011056/1]
- Platform Grant [EP/H00453X/1]
- EU Horizon 2020 Research and Innovation Action Framework Programme (PROGRESS) [637302]
- EPSRC [EP/I011056/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/I011056/1] Funding Source: researchfish
A substantial amount of datasets stored for various applications are often high dimensional with redundant and irrelevant features. Processing and analysing data under such circumstances is time consuming and makes it difficult to obtain efficient predictive models. There is a strong need to carry out analyses for high dimensional data in some lower dimensions, and one approach to achieve this is through feature selection. This paper presents a new relevancy-redundancy approach, called the maximum relevance minimum multicollinearity (MRmMC) method, for feature selection and ranking, which can overcome some shortcomings of existing criteria. In the proposed method, relevant features are measured by correlation characteristics based on conditional variance while redundancy elimination is achieved according to multiple correlation assessment using an orthogonal projection scheme. A series of experiments were conducted on eight datasets from the UCI Machine Learning Repository and results show that the proposed method performed reasonably well for feature subset selection. (C) 2017 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据