A GA-based approach for mining membership functions and concept-drift patterns

Chun Hao Chen; Yu Li; Tzung Pei Hong; Yan Kang Li; Eric Hsueh Chan Lu

doi:10.1109/CEC.2015.7257257

A GA-based approach for mining membership functions and concept-drift patterns

Chun Hao Chen, Yu Li, Tzung Pei Hong, Yan Kang Li, Eric Hsueh Chan Lu

Department of Geomatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2961-2965
Number of pages	5
ISBN (Electronic)	9781479974924
DOIs	https://doi.org/10.1109/CEC.2015.7257257
Publication status	Published - 2015 Sep 10
Event	IEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan Duration: 2015 May 25 → 2015 May 28

Publication series

Name	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Other

Other	IEEE Congress on Evolutionary Computation, CEC 2015
Country	Japan
City	Sendai
Period	15-05-25 → 15-05-28

All Science Journal Classification (ASJC) codes

Computer Science Applications
Computational Mathematics

Access to Document

10.1109/CEC.2015.7257257

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Chen, C. H., Li, Y., Hong, T. P., Li, Y. K., & Lu, E. H. C. (2015). A GA-based approach for mining membership functions and concept-drift patterns. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 2961-2965). [7257257] (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2015.7257257

Chen, Chun Hao ; Li, Yu ; Hong, Tzung Pei ; Li, Yan Kang ; Lu, Eric Hsueh Chan. / A GA-based approach for mining membership functions and concept-drift patterns. 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 2961-2965 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings).

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title = "A GA-based approach for mining membership functions and concept-drift patterns",

abstract = "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.",

author = "Chen, {Chun Hao} and Yu Li and Hong, {Tzung Pei} and Li, {Yan Kang} and Lu, {Eric Hsueh Chan}",

note = "Funding Information: This research was supported by the Ministry of Science and Technology of the Republic of China under grant MOST 103-2221-E-032-029 and 103-2221-E-006-271.; IEEE Congress on Evolutionary Computation, CEC 2015 ; Conference date: 25-05-2015 Through 28-05-2015",

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Chen, CH, Li, Y, Hong, TP, Li, YK & Lu, EHC 2015, A GA-based approach for mining membership functions and concept-drift patterns. in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings., 7257257, 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 2961-2965, IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, 15-05-25. https://doi.org/10.1109/CEC.2015.7257257

A GA-based approach for mining membership functions and concept-drift patterns. / Chen, Chun Hao; Li, Yu; Hong, Tzung Pei; Li, Yan Kang; Lu, Eric Hsueh Chan.

2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 2961-2965 7257257 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

AB - 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.

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Chen CH, Li Y, Hong TP, Li YK, Lu EHC. A GA-based approach for mining membership functions and concept-drift patterns. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 2961-2965. 7257257. (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings). https://doi.org/10.1109/CEC.2015.7257257