AN indicator-based selection multi-objective evolutionary algorithm with preference for multi-class ensemble

Jing-Jing CAO, Sam KWONG, Ran WANG, Ke LI

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)

Abstract

One of the most difficult components for multi-class classification system is to find an appropriate error-correcting output codes (ECOC) matrix, which is used to decompose the multi-class problem into several binary class problems. In this paper, an indicator based multi-objective evolutionary algorithm with preference involved is designed to search the high-quality ECOC matrix. Specifically, the Harrington's one-sided desirability function is integrated into an indicator-based evolutionary algorithm (IBEA), which aims to approximate the relevant regions of pareto front (PF) according to the preference of the decision maker. Simulation results show that the proposed approach has better classification performance than compared multi-class based algorithms
Original languageEnglish
Title of host publicationProceedings - International Conference on Machine Learning and Cybernetics
Pages147-152
DOIs
Publication statusPublished - 13 Jan 2015
Externally publishedYes

Keywords

  • Error-correcting output coding
  • Harrington's one-sided desirability function
  • Indicator-based evolutionary algorithm
  • Multi-class problem
  • Pareto front

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