Simultaneous Feature and Model Selection for High-Dimensional Data

被引：0

作者：

Perolini, Alessandro ^{[1
]}

Guerif, Sebastien ^{[2
,3
]}

机构：

[1] Politecn Milan, Dipart Ingn Gestionale, Piazza L da Vinci 32, I-20133 Milan, Italy

[2] CNRS, Villetaneuse, France

[3] Univ Paris 13, F-75231 Paris 05, France

来源：

2011 23RD IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2011) | 2011年

关键词：

Feature selection; model selection; Support Vector Machines; classification performance; CLASSIFICATION; CANCER;

D O I：

10.1109/ICTAI.2011.16

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The paper proposes an Evolutionary-based method to improve the prediction performance of Support Vector Machines classifiers applied to both artificial and real-world datasets which suffer from the curse of dimensionality. This method performs a simultaneous feature and model selection to discover the subset of features and the SVM parameters' values which provide a low prediction error. Moreover, it does not require a pre-processing step to filter the features so it can be applied to a whole dataset.

引用

页码：47 / 50

页数：4

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