Feature Selection Using Tabu Search with Learning Memory: Learning Tabu Search

被引：7

作者：

Mousin, Lucien ^{[1
]}

Jourdan, Laetitia ^{[1
]}

Marmion, Marie-Eleonore Kessaci ^{[1
]}

Dhaenens, Clarisse ^{[1
]}

机构：

[1] Univ Lille, Cent Lille, Ctr Rech Informat Signal & Automat Lille, CRIStAL,UMR 9189,CNRS, F-59655 Lille, France

来源：

LEARNING AND INTELLIGENT OPTIMIZATION (LION 10) | 2016年 / 10079卷

关键词：

CLASSIFICATION;

D O I：

10.1007/978-3-319-50349-3_10

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Feature selection in classification can be modeled as a combinatorial optimization problem. One of the main particularities of this problem is the large amount of time that may be needed to evaluate the quality of a subset of features. In this paper, we propose to solve this problem with a tabu search algorithm integrating a learning mechanism. To do so, we adapt to the feature selection problem, a learning tabu search algorithm originally designed for a railway network problem in which the evaluation of a solution is time-consuming. Experiments are conducted and show the benefit of using a learning mechanism to solve hard instances of the literature.

引用

页码：141 / 156

页数：16

共 50 条

[41] Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets [J].

Elgamal, Zenab Mohamed ;

Yasin, Norizan Mohd ;

Sabri, Aznul Qalid Md ;

Sihwail, Rami ;

Tubishat, Mohammad ;

Jarrah, Hazim .

COMPUTATION, 2021, 9 (06)

[42] APPLICATION OF AGENT-BASED SIMULATED ANNEALING AND TABU SEARCH PROCEDURES TO SOLVING THE DATA REDUCTION PROBLEM [J].

Czarnowski, Ireneusz ;

Jedrzejowicz, Piotr .

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE, 2011, 21 (01) :57-68

[43] All-relevant feature selection using multidimensional filters with exhaustive search [J].

Mnich, Krzysztof ;

Rudnicki, Witold R. .

INFORMATION SCIENCES, 2020, 524 :277-297

[44] An Efficient Feature Selection using Parallel Cuckoo Search and Naive Bayes classifier [J].

Sujana, T. Sai ;

Rao, N. Madhu Sudana ;

Reddy, Raja Sekar .

2017 INTERNATIONAL CONFERENCE ON NETWORKS & ADVANCES IN COMPUTATIONAL TECHNOLOGIES (NETACT), 2017, :167-172

[45] Hybrid feature selection algorithm using symmetrical uncertainty and a harmony search algorithm [J].

Shreem, Salam Salameh ;

Abdullah, Salwani ;

Nazri, Mohd Zakree Ahmad .

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2016, 47 (06) :1312-1329

[46] Feature Selection for High Dimensional Data Using Monte Carlo Tree Search [J].

Chaudhry, Muhammad Umar ;

Lee, Jee-Hyong .

IEEE ACCESS, 2018, 6 :76036-76048

[47] Feature selection for high dimensional imbalanced class data using harmony search [J].

Moayedikia, Alireza ;

Ong, Kok-Leong ;

Boo, Yee Ling ;

Yeoh, William G. S. ;

Jensen, Richard .

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2017, 57 :38-49

[48] Feature selection for high-dimensional data using a multivariate search space reduction strategy based scatter search [J].

Garcia-Torres, Miguel .

JOURNAL OF HEURISTICS, 2025, 31 (01)

[49] Chaotic binary Group Search Optimizer for feature selection [J].

Abualigah, Laith ;

Diabat, Ali .

EXPERT SYSTEMS WITH APPLICATIONS, 2022, 192

[50] Feature subset selection by gravitational search algorithm optimization [J].

Han, XiaoHong ;

Chang, XiaoMing ;

Quan, Long ;

Xiong, XiaoYan ;

Li, JingXia ;

Zhang, ZhaoXia ;

Liu, Yi .

INFORMATION SCIENCES, 2014, 281 :128-146

← 1 2 3 4 5 →