Distributed MOPSO with a new population subdivision technique for the feature selection

Fdhila, Raja; Hamdani, Tarek M.; Alimi, Adel M.

doi:10.1109/isciii.2011.6069747

Cited by 16 publications

(9 citation statements)

References 12 publications

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“…Some literature contributions focus on the use of selection techniques embedded in the optimization procedure in order to find out only one optimal solution in multi‐objective contexts. To this purpose, Fdhila et al () use subswarms by dynamically subdividing the population in a MOPSO using Pareto front. In addition, Mattson and Messac () compare conceptual design alternatives by means of Pareto frontiers for each set of concepts.…”

Section: Discussionmentioning

confidence: 99%

A Multicriteria Decision‐Making Application to the Conceptual Design of Mechanical Components

Renzi

Leali

2016

Multi Criteria Decision Anal

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Early design is crucial for the success of the final product. In the conceptual design phase, several constraints, criteria, objectives and disciplines have to be considered. To this aim, multidisciplinary optimization has proven effective for the solution of engineering design problems, even in the industrial every-day practice, to improve and simplify the work of designers in a successful quest of the best compromise solution. In this paper, a multicriteria decision-making (MCDM)-based design platform for early optimal design of industrial components is proposed. In a group decision-making context, the selection of the most suitable component among several possible layouts is performed by means of a group Fuzzy Technique for Order of Preference by Similarity to Ideal Solution approach. Hence, a multi-objective optimization is performed on the selected component by applying a multi-objective particle swarm optimization for finding optimal component dimensions. An industrial case study is presented for showing the efficiency of the multicriteria decision-making-based design platform, regarding an innovative and low-cost solution to increase the duration of heel tips in women's shoes.

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Section: Discussionmentioning

confidence: 99%

A Multicriteria Decision‐Making Application to the Conceptual Design of Mechanical Components

Renzi

Leali

2016

Multi Criteria Decision Anal

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“…The proposed algorithm further reduced the number of features and improved the classification performance over the previous method [140] and standard PSO. PSO with multiple swarms to share experience has also been applied to feature selection [11], [180], but may lead to the problem of high computational cost.…”

Section: Table III Categorisation Of Pso Approaches Single Objectivementioning

confidence: 99%

“…For wrapper approaches, many existing works used only the classification performance as the fitness function [11], [134], [135], [137], [138], [139], [140], [142], [160], which led to relatively large feature subsets. However, most of the fitness functions used different ways to combine both the classification performance and the number of features into a single fitness function [70], [136], [141], [180], [148], [181]. However, it is difficult to determine in advance the optimal balance between them without a priori knowledge.…”

Section: Table III Categorisation Of Pso Approaches Single Objectivementioning

confidence: 99%

A Survey on Evolutionary Computation Approaches to Feature Selection

Xue

Zhang

Browne

et al. 2016

IEEE Trans. Evol. Computat.

1,367

635

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“…However, it has not been compared with any wrapper algorithm, which can usually obtain higher classification performance than a filter algorithm. Fdhila et al [21] applied multi-swarm PSO to solve feature selection problems. However, the computational cost of the proposed algorithm is high because it involves parallel evolutionary processes and multiple sub-swarms with a relative large number of particles.…”

Section: B Entropy and Mutual Informationmentioning

confidence: 99%

Filter based backward elimination in wrapper based PSO for feature selection in classification

Nguyen

Xue

Liu

et al. 2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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The advances in data collection increase the dimensionality of the data (i.e. the total number of features) in many fields, which arises a challenge to many existing feature selection approaches. This paper develops a new feature selection approach based on particle swarm optimisation (PSO) and a local search that mimics the typical backward elimination feature selection method. The proposed algorithm uses a wrapper based fitness function, i.e. the classification error rate. The local search is performed only on the global best and uses a filter based measure, which aims to take the advantages of both filter and wrapper approaches. The proposed approach is tested and compared with three recent PSO based feature selection algorithms and two typical traditional feature selection methods. Experiments on eight benchmark datasets show that the proposed algorithm can be successfully used to select a significantly smaller number of features and simultaneously improve the classification performance over using all features. The proposed approach outperforms the three PSO based algorithms and the two traditional methods.

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Distributed MOPSO with a new population subdivision technique for the feature selection

Cited by 16 publications

References 12 publications

A Multicriteria Decision‐Making Application to the Conceptual Design of Mechanical Components

A Multicriteria Decision‐Making Application to the Conceptual Design of Mechanical Components

A Survey on Evolutionary Computation Approaches to Feature Selection

Filter based backward elimination in wrapper based PSO for feature selection in classification

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