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

Daeun Jung, Hyunggon Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Pages523-525
Number of pages3
ISBN (Electronic)9781728113395
DOIs
StatePublished - Jul 2019
Event11th International Conference on Ubiquitous and Future Networks, ICUFN 2019 - Zagreb, Croatia
Duration: 2 Jul 20195 Jul 2019

Publication series

NameInternational Conference on Ubiquitous and Future Networks, ICUFN
Volume2019-July
ISSN (Print)2165-8528
ISSN (Electronic)2165-8536

Conference

Conference11th International Conference on Ubiquitous and Future Networks, ICUFN 2019
Country/TerritoryCroatia
CityZagreb
Period2/07/195/07/19

Keywords

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

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