A GA-based Approach for Mining Membership Functions and Concept-Drift Patterns

被引：0

作者：

Chen, Chun-Hao ^{[1
]}

Li, Yu ^{[2
]}

Hong, Tzung-Pei ^{[2
,3
]}

Li, Yan-Kang ^{[2
,3
]}

Lu, Eric Hsueh-Chan ^{[4
]}

机构：

[1] Tamkang Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan

[2] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung, Taiwan

[3] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung, Taiwan

[4] Natl Cheng Kung Univ, Dept Geomat, Tainan, Taiwan

来源：

2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2015年

关键词：

concept drift; data mining; fuzzy association rules; genetic algorithms; membership functions; FUZZY ASSOCIATION RULES; GENETIC ALGORITHMS; CLASSIFICATION; CONTEXTS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Since customers' behaviors may change over time in real applications, algorithms that can be utilized to mine these drift patterns are needed. In this paper, we propose a GA-based approach for mining fuzzy concept-drift patterns. It consists of two phases. The first phase mines membership functions and the second one finds fuzzy concept-drift patterns. In the first phase, appropriate membership functions for items are derived by GA with a designed fitness function. Then, the derived membership functions are utilized to mine fuzzy concept-drift patterns in the second phase. Experiments on simulated datasets are also made to show the effectiveness of the proposed approach.

引用

页码：2961 / 2965

页数：5

共 24 条

[1] Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms [J].

Alcala-Fdez, Jesus ;

Alcala, Rafael ;

Jose Gacto, Maria ;

Herrera, Francisco .

FUZZY SETS AND SYSTEMS, 2009, 160 (07) :905-921

[2]

[Anonymous], 1999, INTELL DATA ANAL

[3]

Black M, 2003, SOFT COMPUT, V8, P102, DOI [10.1007/s00500-002-0250-2, 10.1007/S00500-002-0250-2]

[4] Mining association rules with weighted items [J].

Cai, CH ;

Fu, AWC ;

Cheng, CH ;

Kwong, WW .

IDEAS 98 - INTERNATIONAL DATABASE ENGINEERING AND APPLICATIONS SYMPOSIUM, PROCEEDINGS, 1998, :68-77

[5] MOGA-based fuzzy data mining with taxonomy [J].

Chen, Chun-Hao ;

He, Ji-Syuan ;

Hong, Tzung-Pei .

KNOWLEDGE-BASED SYSTEMS, 2013, 54 :53-65

[6]

Chunhua J., 2011, J CONVERGENCE INFORM, V6, P235

[7]

Gama J, 2004, LECT NOTES ARTIF INT, V3171, P286

[8] A survey of emerging patterns for supervised classification [J].

Garcia-Borroto, Milton ;

Fco. Martinez-Trinidad, Jose ;

Ariel Carrasco-Ochoa, Jesus .

ARTIFICIAL INTELLIGENCE REVIEW, 2014, 42 (04) :705-721

[9] Fuzzy connectives based crossover operators to model genetic algorithms population diversity [J].

Herrera, F ;

Lozano, M ;

Verdegay, JL .

FUZZY SETS AND SYSTEMS, 1997, 92 (01) :21-30

[10] A GA-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership functions [J].

Hong, Tzung-Pei ;

Chen, Chun-Hao ;

Wu, Yu-Lung ;

Lee, Yeong-Chyi .

SOFT COMPUTING, 2006, 10 (11) :1091-1101

← 1 2 3 →