A fuzzy genetic programming-based algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans

Carmona, C. J.; Ruiz‐Rodado, Victor; Jesus, María José del; Weber, Anke; Grootveld, Martin; González, Pedro; Elizondo, David

doi:10.1016/j.ins.2014.11.030

Cited by 42 publications

(24 citation statements)

References 36 publications

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“…MEMFIS effectively generates and trains first‐order Takagi–Sugeno fuzzy systems for regression and control. This new approach is used in regression and control benchmarks, such as the inverted pendulum, comparing favorably to its peers. Carmona et al (): presents a new approach named Fuzzy Genetic Programming‐based for Subgroup Discovery: FuGePSD. This algorithm represents an EFS based on GP, employing the GCCL approach where rules of the population cooperate and compete between them to obtain optimal solution.…”

Section: Genetic Programming‐based Efsmentioning

confidence: 99%

Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective

Koshiyama

Tanscheit

Vellasco

2018

WIREs Data Min & Knowl

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Studies in Evolutionary Fuzzy Systems (EFSs) began in the 90s and have experienced a fast development since then, with applications to areas such as pattern recognition, curve‐fitting and regression, forecasting and control. An EFS results from the combination of a Fuzzy Inference System (FIS) with an Evolutionary Algorithm (EA). This relationship can be established for multiple purposes: fine‐tuning of FIS's parameters, selection of fuzzy rules, learning a rule base or membership functions from scratch, and so forth. Each facet of this relationship creates a strand in the literature, as membership function fine‐tuning, fuzzy rule‐based learning, and so forth and the purpose here is to outline some of what has been done in each aspect. Special focus is given to Genetic Programming‐based EFSs by providing a taxonomy of the main architectures available, as well as by pointing out the gaps that still prevail in the literature. The concluding remarks address some further topics of current research and trends, such as interpretability analysis, multiobjective optimization, and synthesis of a FIS through Evolving methods. This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning

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Section: Genetic Programming‐based Efsmentioning

confidence: 99%

Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective

Koshiyama

Tanscheit

Vellasco

2018

WIREs Data Min & Knowl

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“…This value represents the balance between the coverage of the pattern,

((), \frac{p + n}{T})

and its accuracy gain, i.e., the comparison of the accuracy obtained with respect of a naive classification by considering all examples as positive

((), \frac{p}{p + n} - \frac{P}{T})

. It is normalized because the domain of this measure depends on the class analyzed . A high value can be interpreted as a high balance between the generality of the pattern and its confidence.…”

Section: Emerging Pattern Miningmentioning

confidence: 99%

An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects

García-Vico

Carmona

Martín

et al. 2017

WIREs Data Min & Knowl

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Emerging pattern mining is a data mining task that aims to discover discriminative patterns, which can describe emerging behavior with respect to a property of interest. In recent years, the description of datasets has become an interesting field due to the easy acquisition of knowledge by the experts. In this review, we will focus on the descriptive point of view of the task. We collect the existing approaches that have been proposed in the literature and group them together in a taxonomy in order to obtain a general vision of the task. A complete empirical study demonstrates the suitability of the approaches presented. This review also presents future trends and emerging prospects within pattern mining and the benefits of knowledge extracted from emerging patterns.

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“…In this domain, another interesting work [38] has been proposed, which discovers the subgroups of patients visiting the psychiatric emergency department. This work implements an evolutionary algorithm SDIGA to discover the fuzzy rule that states the relationship among the arrival time of different patients at this unit.…”

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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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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A fuzzy genetic programming-based algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans

Cited by 42 publications

References 36 publications

Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective

Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective

An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects

Subgroup Discovery Algorithms: A Survey and Empirical Evaluation

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