An iterative Algorithm of Key Feature Selection for Multi-class Classification

被引：0

作者：

Jung, Daeun ^{[1
]}

Park, Hyunggon ^{[1
]}

机构：

[1] Ewha Womans Univ, Dept Elect & Elect Engn, Seoul, South Korea

来源：

2019 ELEVENTH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS (ICUFN 2019) | 2019年

基金：

新加坡国家研究基金会;

关键词：

multi-class classification; machine learning; omics data; feature extraction; feature selection;

D O I：

10.1109/icufn.2019.8806074

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 an iterative algorithm of key feature selection for multi-class classification problems, where the data includes a large number of features but the amount of data is limited. For efficient classification, the proposed algorithm first extracts a set of key feature candidates based on Boruta algorithm and then iteratively adopts conventional machine learning based classification algorithms to determine key features. Simulation results show that the proposed algorithm can effectively determine key features, leading to improved classification accuracy compared to direct adoption of multi-class classification algorithms.

引用

页码：523 / 525

页数：3

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