Name
Abstract | ||
|---|---|---|
Feature selection is crucial, particularly for processing high-dimensional data. Existing selection methods generally compute a discriminant value for a feature with respect to class variable to indicate its classification ability. However, a scalar value can hardly reveal the multifaceted classification abilities of a feature for different subproblems of a complicated multiclass problem. In view of this, we propose to select features based on discrimination structure complementarity. To this end, the classification abilities of a feature for different subproblems are evaluated individually. Consequently, a discrimination structure vector can be obtained to indicate if the feature is discriminative respectively for different subproblems. Based on discrimination structure, indispensable and dispensable features (ID-features for short) are defined. In selection process, the ID-features, which are complementary in discrimination structure to the selected ones, are selected. The proposed method tries to equally treat all subproblems and hence can avoid falling into the pitfall that the discriminative features for difficult subproblems are prone to be covered by the features for easy ones in multi-class classification. Two algorithms are developed and compared with several feature selection methods using some open data sets. Experimental results demonstrate the effectiveness of the proposed method. |
Year | Venue | Keywords |
|---|---|---|
2017 | COMPUTATIONAL INTELLIGENCE | discrimination structure complementarity,feature selection,multiclass classification,variable complementarity |
Field | DocType | Volume |
Complementarity (molecular biology),Open data,Feature selection,Pattern recognition,Computer science,Discriminant,Artificial intelligence,Discriminative model,Scalar Value,Class variable,Machine learning,Multiclass classification | Journal | 33.0 |
Issue | ISSN | Citations |
4.0 | 0824-7935 | 0 |
PageRank | References | Authors |
0.34 | 53 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuqin Wang | 1 | 4 | 1.13 |
| Jinmao Wei | 2 | 23 | 6.46 |
| Zhenglu Yang | 3 | 257 | 35.45 |