Name
Abstract | ||
|---|---|---|
Feature selection is a classical problem in machine learning, and how to design a method to select the features that can contain all the internal semantic correlation of the original feature set is a challenge. The authors present a general approach to select features via rough set based reduction, which can keep the selected features with the same semantic correlation as the original feature set. A new concept named inconsistency is proposed, which can be used to calculate the positive region easily and quickly with only linear temporal complexity. Some properties of inconsistency are also given, such as the monotonicity of inconsistency and so forth. The authors also propose three inconsistency based attribute reduction generation algorithms with different search policies. Finally, a "mini-saturation" bias is presented to choose the proper reduction for further predictive designing. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.4018/jcini.2010040106 | IJCINI |
Keywords | Field | DocType |
feature selection,different search policy,original feature set,semantic correlation,rough set,attribute reduction generation algorithm,feature reduction,proper reduction,internal semantic correlation,selected feature,classical problem,reduction,bias,approximation,granular | Temporal complexity,Data mining,Monotonic function,Pattern recognition,Feature selection,Computer science,Rough set,Feature set,Correlation,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
4 | 2 | 1557-3958 |
Citations | PageRank | References |
5 | 0.41 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Liu | 1 | 213 | 45.82 |
| Yunliang Jiang | 2 | 134 | 22.20 |
| Jianhua Yang | 3 | 5 | 0.41 |