Evolutionary fuzzy rule extraction for subgroup discovery in a psychiatric emergency department

Carmona, C. J.; González, Pedro; Navío-Acosta, Mercedes; Jiménez-Treviño, Luís

doi:10.1007/s00500-010-0670-3

Cited by 49 publications

(13 citation statements)

References 52 publications

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“…In medical domain, subgroup discovery has been widely applied to detect the risk groups with coronary heart disease. In [3,7,[36][37], some influencing factors (such as high cholesterol level, density of lipoprotein, and triglyceride) have been detected for the patients at high risk. All these studies implement SD algorithm for extracting the risk factors as the evaluation of such properties needs the involvement of domain experts for effectively searching the hypothesis space.…”

Section: Applications In Different Domainsmentioning

confidence: 98%

“…Subgroup discovery has addressed different real-world problems in the bio-informatics domain. In [37,40], relevant features are extracted by implementing the SD subgroup discovery algorithm for the detection of different cancer types. [41] employs an SD approach SD4TS (Subgroup Discovery for Test Selection) for breast cancer diagnosis.…”

Section: Applications In Different Domainsmentioning

confidence: 99%

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Subgroup Discovery Algorithms: A Survey and Empirical Evaluation

Helal

2016

J. Comput. Sci. Technol.

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Subgroup discovery is a data mining technique that discovers interesting associations among different variables with respect to a property of interest. Existing subgroup discovery methods employ different strategies for searching, pruning and ranking subgroups. It is very crucial to learn which features of a subgroup discovery algorithm should be considered for generating quality subgroups. In this regard, a number of reviews have been conducted on subgroup discovery. Although they provide a broad overview on some popular subgroup discovery methods, they employ few datasets and measures for subgroup evaluation. In the light of the existing measures, the subgroups cannot be appraised from all perspectives. Our work performs an extensive analysis on some popular subgroup discovery methods by using a wide range of datasets and by defining new measures for subgroup evaluation. The analysis result will help with understanding the major subgroup discovery methods, uncovering the gaps for further improvement and selecting the suitable category of algorithms for specific application domains.

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Section: Applications In Different Domainsmentioning

confidence: 98%

Section: Applications In Different Domainsmentioning

confidence: 99%

Subgroup Discovery Algorithms: A Survey and Empirical Evaluation

Helal

2016

J. Comput. Sci. Technol.

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“…SD is broadly applicable such as in medicine 11 or elearning 41 among others, and focus its interest on partial relations instead of complete ones. The discovered subgroups should be interpretable and interesting according to the criteria of the user.…”

Section: Introductionmentioning

confidence: 99%

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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“…The extraction of subgroups of interest has been also considered as a multi-objective methodology 5 , optimizing more than one quality measure at time. In multi-objective optimization, solutions of a specific iteration are organized in fronts 9 based on the objectives to be optimized.…”

Section: Related Workmentioning

confidence: 99%

“…In this experimental stage, a series of SD algorithms were compared in detail, including NMEEF-SD 3 , SDIGA 10 and MESDIF 5 . Additionally, classic SD algorithms such as CN2-SD 20 and Apriori-SD 17 , were also included in the study.…”

Section: Performance Of the Proposed Algorithmmentioning

confidence: 99%

Recommending degree studies according to students’ attitudes in high school by means of subgroup discovery

Noaman¹,

Luna²,

Ragab³

et al. 2016

IJCIS

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The transition from high school to university is a critical step and many students head toward failure just because their final degree option was not the right choice. Both students' preferences and skills play an important role in choosing the degree that best fits them, so an analysis of these attitudes during the high school can minimize the drop out in a posteriori learning period like university. We propose a subgroup discovery algorithm based on grammars to extract itemsets and relationships that represent any type of homogeneity and regularity in data from a supervised context. This supervised context is cornerstone, considering a single item or a set of them as interesting and distinctive. The proposed algorithm supports the students' final degree decision by extracting relations among different students' skills and preferences during the high school period. The idea is to be able to provide advices with regard to what is the best degree option for each specific skill and student. In this regard, the use of grammars is essential since it enables subjective and external knowledge to be included during the mining process. The proposed algorithm has been compared against different subgroup discovery algorithms, achieving excellent results. A real-world experimental analysis has been developed at King Abdulaziz University, one of the most important universities in Saudi Arabia, where there is a special interest in introducing models to understand the students' skills to guide them accordingly.

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Evolutionary fuzzy rule extraction for subgroup discovery in a psychiatric emergency department

Cited by 49 publications

References 52 publications

Subgroup Discovery Algorithms: A Survey and Empirical Evaluation

Subgroup Discovery Algorithms: A Survey and Empirical Evaluation

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

Recommending degree studies according to students’ attitudes in high school by means of subgroup discovery

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