Multi-Objective Evolutionary Optimization Algorithms for Machine Learning: A Recent Survey

被引：29
|
作者：
Alexandropoulos, Stamatios-Aggelos N. [1 ]
Aridas, Christos K. [1 ]
Kotsiantis, Sotiris B. [1 ]
Vrahatis, Michael N. [1 ]
机构：
[1] Univ Patras, Dept Math, Computat Intelligence Lab, CILab, Patras, Greece
来源：
APPROXIMATION AND OPTIMIZATION: ALGORITHMS, COMPLEXITY AND APPLICATIONS | 2019年 / 145卷
关键词：
SIMULTANEOUS FEATURE-SELECTION; NUMERICAL ASSOCIATION RULES; GENETIC ALGORITHM; DIFFERENTIAL EVOLUTION; CLASSIFIER ENSEMBLES; INSTANCE SELECTION; DECISION TREES; NETWORKS; DESIGN;
D O I：
10.1007/978-3-030-12767-1_4
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
The machine learning algorithms exploit a given dataset in order to build an efficient predictive or descriptive model. Multi-objective evolutionary optimization assists machine learning algorithms to optimize their hyper-parameters, usually under conflicting performance objectives and selects the best model for a given task. In this paper, recent multi-objective evolutionary approaches for four major data mining and machine learning tasks, namely: (a) data preprocessing, (b) classification, (c) clustering, and (d) association rules, are surveyed.
引用
收藏
页码：35 / 55
页数：21
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