USING A PENALIZED MAXIMUM LIKELIHOOD MODEL FOR FEATURE SELECTION

被引：0

作者：

Jalalirad, Amir ^{[1
]}

Tjalkens, Tjalling ^{[1
]}

机构：

[1] Eindhoven Univ Technol, Dept Elect Engn, POB 513, NL-5600 MB Eindhoven, Netherlands

来源：

2015 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING | 2015年

关键词：

model selection; feature selection; data classification; sequence probability estimation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Feature selection and learning through selected features are the two steps that are generally taken in classification applications. Commonly, each of these tasks are dealt with separately. In this paper, we introduce a method that optimally combines feature selection and learning through feature-based models. Our proposed method implicitly removes redundant and irrelevant features as it searches through a comprehensive class of models and picks the penalized maximum likelihood model. The method is proved to be efficient in terms of the reduction of the calculation complexity and the accuracy in the classification of artificial and real data.

引用

页数：6

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