Paper Info
Title
AN indicator-based selection multi-objective evolutionary algorithm with preference for multi-class ensemble
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.
Year
DOI
Venue
2014
10.1109/ICMLC.2014.7009108
ICMLC
Keywords
Field
DocType
indicator-based evolutionary algorithm,evolutionary computation,multiclass problem,error-correcting output coding,indicator-based selection multiobjective evolutionary algorithm,binary class problems,pattern classification,harrington's one-sided desirability function,matrix algebra,appropriate error-correcting output codes matrix,error correction codes,pareto analysis,ecoc matrix,multiclass based algorithms,multiclass classification system,decision maker,pf,multiclass ensemble,one-sided desirability function,multi-class problem,ibea,pareto front,accuracy
Mathematical optimization,Pattern recognition,Evolutionary algorithm,Computer science,Matrix (mathematics),Multi-objective optimization,Artificial intelligence,Machine learning,Decision maker,Desirability function,Binary number
Conference
Volume
ISSN
ISBN
1
2160-133X
978-1-4799-4216-9
Citations 
PageRank 
References 
0
0.34
14
Authors
4
Name
Order
Citations
PageRank
Jingjing Cao11315.52
Sam Kwong24590315.78
Ran Wang343924.42
Ke Li479829.81