New feature selection and voting scheme to improve classification accuracy

Research output: Contribution to journalArticle

Abstract

Classification is a classic technique employed in data mining. Many ensemble learning methods have been introduced to improve the predictive accuracy of classification. A typical ensemble learning method consists of three steps: selection, building, and integration. Of the three steps, the first and third significantly affect the predictive accuracy of the classification. In this paper, we propose a new selection and integration scheme. Our method can improve the accuracy of subtrees and maintain their diversity. Through a new voting scheme, the predictive accuracy of ensemble learning is improved. We also theoretically analyzed the selection and integration steps of our method. The results of experimental analyses show that our method can achieve better accuracy than two state-of-the-art tree-based ensemble learning approaches.

Original languageEnglish
JournalSoft Computing
DOIs
Publication statusAccepted/In press - 2019 Jan 1

Fingerprint

Ensemble Learning
Voting
Feature Selection
Feature extraction
Data mining
Data Mining

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

Cite this

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New feature selection and voting scheme to improve classification accuracy. / Tsai, Cheng Jung.

In: Soft Computing, 01.01.2019.

Research output: Contribution to journalArticle

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