Bayesian Top Scoring Pairs for Feature Selection

被引：0

作者：

Arslan, Emre ^{[1
]}

Braga-Neto, Ulisses M. ^{[1
]}

机构：

[1] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX USA

来源：

2017 FIFTY-FIRST ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS | 2017年

关键词：

Bayesian Top Scoring Pairs; Dimensionality Reduction; Feature Selection; SUPPORT VECTOR MACHINES; GENE SELECTION; CANCER; CLASSIFICATION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We propose a novel feature selection approach based on the Bayesian Top Scoring Pairs (BTSP) method. We compare its performance against well-known feature selection methods, under SVM, k-NN and NB classification rules, by means of an extensive numerical experiment using real gene-expression data sets. Results demonstrate the promise of the BTSP feature selection approach in the analysis of high-dimensional biological data.

引用

页码：387 / 391

页数：5

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