Dimensionality Reduction using Symbolic Regression

被引：0

作者：

Icke, Ilknur ^{[1
]}

Rosenberg, Andrew ^{[1
]}

机构：

[1] CUNY, Grad Ctr, New York, NY 10016 USA

来源：

GECCO-2010 COMPANION PUBLICATION: PROCEEDINGS OF THE 12TH ANNUAL GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2010年

关键词：

symbolic regression; dimensionally reduction; classification;

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：

In this paper, we propose a symbolic regression approach for data visualisation that is suited for classification tasks. Our algorithm seeks a visually and semantically interpretable lower dimensional representation of the given dataset that would increase classifier accuracy as well. This simultaneous identification of easily interpretable dimensionality reduction and improved classification accuracy relieves the user of the burden of experimenting with the many combinations of classification and dimensionality reduction techniques.

引用

页码：2085 / 2086

页数：2

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