Paper Info
Title
A Hybrid Feature Selection Algorithm Applied to High-dimensional Imbalanced Small-sample Data Classification
Abstract
With the rapid development of microarray technology and interdisciplinary science, it is possible for microarray technology to be used to predict diseases. Microarray technology has the advantages of high speed, high efficiency and reliability in disease prediction. However, microarray data are usually high-dimensional with small samples, additionally, the samples are often imbalanced, which brings a lot of difficulties to researchers. In view of the above problems, it is proposed in this paper a Filter-Wrapper hybrid feature selection algorithm Union Information Gini Cost-sensitive Feature Selection General Vector Machine (UIG-CFGVM) to tackle the high-dimensional imbalanced small-sample problem. The improved hybrid algorithm is as follows: Firstly, the most common features are removed by the proposed hybrid filter algorithm UIG, which is obtained by Information Gain (Info)and Gini Index (Gini). Secondly, Cost-sensitive Feature selection General Vector Machine (CFGVM) is used as Wrapper method to further improve the performance of the algorithm. The experimental results show that the proposed algorithm UIG-CFGVM has better classification performance in seven biomedical high-dimensional imbalanced small-sample datasets compared with other similar algorithms.
Year
DOI
Venue
2019
10.1109/APSIPAASC47483.2019.9023210
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
Keywords
DocType
ISSN
Filter algorithm,Wrapper algorithm,Feature selection,High-dimensional Imbalanced Small-sample data
Conference
2309-9402
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Fang Feng100.34
Qingquan Lv200.34
Mingsong Wang300.34
Xuhui Yang400.34
Qingguo Zhou510329.48
Rui Zhou6206.92