A model based on ant colony system and rough set theory to feature selection

Bello, Rafael; Nowé, Ann; Caballero, Yailé; Gómez, Yudel; Vrancx, Peter

doi:10.1145/1068009.1068053

Cited by 31 publications

(24 citation statements)

References 3 publications

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“…Among them one can find the "quality of classification", displayed in expression (1). It quantifies the percentage of objects which are correctly classified into the given decision classes Y = {Y 1 , .…”

Section: Rough Set Theory: Basic Conceptsmentioning

confidence: 99%

“…For this study we used the ACS-RST-FS according to results showed in [1] and [2]. Let A = {a 1 , a 2 , . .…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

“…Concerning ACO, the Ant Colony System (ACS) model was picked for benchmarking following the advice in [1] and [2], for it reported the most encouraging outcomes. As to the parameter setting, we stuck to the guidelines provided in the aforesaid studies, i.e.…”

Section: A Comparative Studymentioning

confidence: 99%

“…Several categories of evaluation functions stand nowadays, like distance measures, information measures (e.g., entropy), dependency measures, consistency measures and classification error measures [8]. More recently, the "quality of the classification" measure borrowed from rough set theory (RST) has been employed as a numerical estimator of the quality of reducts [11,1,2,24,25]. A reduct is a minimal subset of attributes which preserves the partition over a universe, as stated in [14] wherein also the role played by reducts in feature selection and reduction is explained.…”

Section: Introductionmentioning

confidence: 99%

“…Some of them have exhibited good results, mainly attained by using ACO-or PSO-based approaches, such as [11,1,2,24,25,26]. In [3] and [4], a new approach to feature selection based on the ACO and PSO methodologies is presented.…”

Section: Introductionmentioning

confidence: 99%

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Rough Sets and Evolutionary Computation to Solve the Feature Selection Problem

Bello

Gómez

Caballero

et al.

Studies in Computational Intelligence

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Summary. The feature selection problem has been usually addressed through heuristic approaches given its significant computational complexity. In this context, evolutionary techniques have drawn the researchers' attention owing to their appealing optimization capabilities. In this chapter, promising results achieved by the authors in solving the feature selection problem through a joint effort between rough set theory and evolutionary computation techniques are reviewed. In particular, two new heuristic search algorithms are introduced, i.e. Dynamic Mesh Optimization and another approach which splits the search process carried out by swarm intelligence methods.

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Section: Rough Set Theory: Basic Conceptsmentioning

confidence: 99%

“…For this study we used the ACS-RST-FS according to results showed in [1] and [2]. Let A = {a 1 , a 2 , . .…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Section: A Comparative Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rough Sets and Evolutionary Computation to Solve the Feature Selection Problem

Bello

Gómez

Caballero

et al.

Studies in Computational Intelligence

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A decision support system for diagnosis of hepatitis disease using an integrated rough set and fish swarm algorithm

Kumari

Acharjya

2022

Concurrency and Computation

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Healthcare data analysis is a primary concern. It leads to multiple levels of knowledge extraction for decision support systems because of the presence of uncertainties.Therefore, this paper integrates the rough set and artificial fish swarm optimization to develop a decision support system that handles uncertainties present in an information system. In the initial stage, the artificial fish swarm-the rough set procedure is implemented in finding vital features. Further, in the second phase, the rough set uses these vital features to develop a decision support system. The above model is analyzed over hepatitis B disease. The proposed model attains an accuracy of 92.4%. Further, the proposed model is compared with the classical rough set, decision tree, and artificial fish swarm-decision tree model. The accuracy obtained is 88.9%, 83.3%, and 90.8%, respectively. The proposed model has a greater accuracy of 3.5% than the rough set model and has a greater accuracy of 9.1% than the decision tree model. Simultaneously, the proposed model has 1.6% greater accuracy than the artificial fish swarm-decision tree model. Therefore, it is believed that the projected decision support system may be used to prevent and detect hepatitis B diseases.

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