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Feature Selection via Discretization (1997)
| Content Provider | CiteSeerX |
|---|---|
| Author | Liu, Huan Setiono, Rudy |
| Abstract | Discretization can turn numeric attributes into discrete ones. Feature selection can eliminate some irrelevant and/or redundant attributes. Chi2 is a simple and general algorithm that uses the 2 statistic to discretize numeric attributes repeatedly until some inconsistencies are found in the data. It achieves feature selection via discretization. It can handle mixed attributes, work with multiclass data, and remove irrelevant and redundant attributes. Keywords--- discretization, feature selection, pattern classification I. Introduction Feature selection can eliminate some irrelevant and/or redundant attributes. By using relevant features, classification algorithms can in general improve their predictive accuracy, shorten the learning period, and form simpler concepts. There are abundant feature selection algorithms. Some use methods like principle component to compose a smaller number of new features [11,12]; some select a subset of the original attributes [1,5]. This paper consi... |
| File Format | |
| Journal | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING |
| Journal | IEEE Transactions on knowledge and data engineering |
| Journal | IEEE transactions on knowledge and data engineering |
| Journal | IEEE Transactions on Knowledge and Data Engineering |
| Publisher Date | 1997-01-01 |
| Access Restriction | Open |
| Subject Keyword | Discrete One Mixed Attribute Predictive Accuracy Classification Algorithm Keywords Discretization New Feature Numeric Attribute Feature Selection Pattern Classification Simpler Concept Original Attribute General Algorithm Abundant Feature Selection Algorithm Principle Component Multiclass Data Introduction Feature Selection Learning Period Relevant Feature Paper Consi Redundant Attribute |
| Content Type | Text |
| Resource Type | Article |
