摘要
In this paper, a new feature selection method based on generalized data field(FR-GDF) is proposed. The goal of feature selection is selecting useful features and simultaneously excluding garbage features from a given feature set. It is Important to measure the “distance” between data points in existing feature selection approaches. To measure the “distance”, FR-GDF adopts potential value of data field. Information entropy of potential value is used to measure the inter-class distance and intra-class distance. This method eliminates unimportant or noise features of original feature sets and extracts the optional features. Experiments prove that FR-GDF algorithm performs well and is independent of the specific classification algorithm.
| 源语言 | 英语 |
|---|---|
| 页（从-至） | 319-333 |
| 页数 | 15 |
| 期刊 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| 卷 | 8933 |
| DOI | |
| 出版状态 | 已出版 - 2014 |
指纹
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Zhao, L., Wang, S., & Lin, Y. (2014). A new filter approach based on generalized data field. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8933, 319-333. https://doi.org/10.1007/978-3-319-14717-8_25