NMEEF-SD: Non-dominated Multiobjective Evolutionary Algorithm for Extracting Fuzzy Rules in Subgroup Discovery

Carmona, C. J.; González, Pedro; Herrera, F.

doi:10.1109/tfuzz.2010.2060200

Cited by 103 publications

(44 citation statements)

References 46 publications

(26 reference statements)

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“…From the plethora of MOEAs, the Nondominated Sorting Genetic Algorithm II (NSGA-II) (Deb et al, 2002) was selected for its popularity (Alcalá et al, 2007a) and ability to maintain a diverse set of solutions suitable for extracting multiple patterns. Previous works have used the NSGA-II for subgroup discovery (Carmona et al, 2010), a closely related area, motif sequence discovery (Kaya, 2009) …”

Section: Multi-objective Evolutionary Search and Optimisationmentioning

confidence: 99%

Evolutionary algorithms and fuzzy sets for discovering temporal rules

Matthews¹,

Góngora²,

Hopgood³

2013

International Journal of Applied Mathematics and Computer Science

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A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method's ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.

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Section: Multi-objective Evolutionary Search and Optimisationmentioning

confidence: 99%

Evolutionary algorithms and fuzzy sets for discovering temporal rules

Matthews¹,

Góngora²,

Hopgood³

2013

International Journal of Applied Mathematics and Computer Science

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“…A complete study of this algorithm with respect to other SD approaches using different quality measures, including a statistical analysis, can be observed in 10 .…”

Section: Nmeef-sd Algorithmmentioning

confidence: 99%

“…In addition, several EFSs have been proposed for the SD task -as SDIGA 19 or MESDIF 9 -. The latter EFS proposed so far for the SD task is the Non-dominated Multi-objective Evolutionary algorithm for Extracting Fuzzy rules in Subgroup Discovery (NMEEF-SD) 10 , whose objective is the extraction of descriptive fuzzy and/or crisp rules for SD, depending on the type of variables present in the problem.…”

Section: Introductionmentioning

confidence: 99%

“…This process of contextualizing the membership functions enables to achieve a better covering degree while maintaining the original shapes. The main objective is to analyse the use of this process on the rules obtained by the SD algorithm NMEEF-SD 10 . This algorithm has been used to obtain the rule base, and then a lateral tuning of the membership functions has been performed.…”

Section: Introductionmentioning

confidence: 99%

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Genetic lateral tuning for subgroup discovery with fuzzy rules using the algorithm NMEEF-SD

Carmona¹,

González²,

Gacto³

et al. 2012

IJCIS

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The main objective of subgroup discovery is to discover interesting and interpretable patterns with respect to a specific property. The use of evolutionary fuzzy systems provides good algorithms to approach this problem. In this sense, NMEEF-SD algorithm -one of the most representative evolutionary fuzzy systems for subgroup discovery-obtains precise and interpretable subgroups. However in the majority of the evolutionary fuzzy systems, the membership functions of the linguistic labels are usually fixed to static values and the partitions are not adapted to the context of each variable. In this paper, a post-processing tuning step to improve the results of the subgroup discovery algorithm NMEEF-SD is proposed, allowing the partitions to be adapted to the context the variables. The application of this tuning step is a novelty in subgroup discovery and consist of a genetic algorithm which allows the lateral displacement of the membership functions of a label considering a unique parameter, using the 2-tuples linguistic representation. The results obtained using different data sets of the KEEL repository show the improvement in the performance of the NMEEF-SD algorithm with lateral displacement. The study is supported by statistical tests to improve the analysis performed.

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“…From the plethora of MOEAs, we selected NSGA-II [16] for its popularity and ability to maintain a diverse set of solutions suitable for extracting multiple patterns. Previous works have used NSGA-II for Subgroup Discovery [17], a closely related area, and motif sequence discovery [18], a different form of temporal mining.…”

Section: Multi-objective Evolutionary Search and Optimisationmentioning

confidence: 99%

Evolving Temporal Fuzzy Association Rules from Quantitative Data with a Multi-Objective Evolutionary Algorithm

Matthews

Góngora

Hopgood

2011

Lecture Notes in Computer Science

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Abstract. A novel method for mining association rules that are both quantitative and temporal using a multi-objective evolutionary algorithm is presented. This method successfully identifies numerous temporal association rules that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy association rules and the approach of using a hybridisation of a multi-objective evolutionary algorithm with fuzzy sets. Results show the ability of a multi-objective evolutionary algorithm (NSGA-II) to evolve multiple target itemsets that have been augmented into synthetic datasets.

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NMEEF-SD: Non-dominated Multiobjective Evolutionary Algorithm for Extracting Fuzzy Rules in Subgroup Discovery

Cited by 103 publications

References 46 publications

Evolutionary algorithms and fuzzy sets for discovering temporal rules

Evolutionary algorithms and fuzzy sets for discovering temporal rules

Genetic lateral tuning for subgroup discovery with fuzzy rules using the algorithm NMEEF-SD

Evolving Temporal Fuzzy Association Rules from Quantitative Data with a Multi-Objective Evolutionary Algorithm

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