A Modified Electromagnetic-Like Mechanism for Rough Set Attribute Reduction

Abdolrazzagh-Nezhad, Majid; Izadpanah, Shaghayegh

doi:10.1007/978-3-319-46254-7_28

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…The application of meta-heuristic algorithms for rough set feature selection has been studied in-depth in the literature and has shown promising results. Some examples can be found in [17,[61][62][63][64][65][66][67][68][69]. The results of more recent research in this area can be found in [70,71].…”

Section: Related Workmentioning

confidence: 99%

“…Nature-inspired meta-heuristic algorithms, which are based on the behavior of physical or biological systems, have become particularly successful in many fields and optimization problems over the past years. Some applications of optimization problems include: timetabling and scheduling [1][2][3][4], industry [5][6][7], data mining [8][9][10][11][12][13][14][15][16][17][18][19], engineering [20][21][22], pattern recognition [23][24][25][26], and economics [27,28]. In the field of data mining, classification is one of the key tasks and feature selection is an important pre-processing step for successful classification.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Auction-Based Optimization Algorithm and Its Application in Rough Set Feature Selection

Jaddi

Abdullah

2021

IEEE Access

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The selection of features from data, as one of the most important tasks in data mining, strongly affects the accuracy of classification. The removal of irrelevant and redundant features from data while simultaneously avoiding information loss is the main objective of feature selection. Feature selection is possible using rough set theory and meta-heuristic algorithms. In this paper, a novel auctionbased optimization algorithm (ABOA) is proposed to contribute to generating an effective algorithm with a good trade-off between exploration and exploitation. This new algorithm simulates the auction sale process, where bidders offer higher/lower amounts to outbid each other. Auctions are categorized into ascending auctions and descending auctions and thus respectively represent maximization and minimization problems in ABOA. In the first step of the ABOA after initialization, a predefined number of bidders is selected and an auction is performed between them. The winner is selected and another auction is performed between the winner and a predefined number of the winner's neighbors. The winner of this round of auction is added to the winner list. This process is iterated until a predefined number of winners is found. Finally, one more auction is performed between all the winners on the winner list and the winner of that auction becomes the final winner. The algorithm with different parameter setting scenarios is tested on 25 benchmark test functions. The algorithm with the best results is then used to perform feature selection on 18 UCI datasets. The feature selection and classification accuracy results are compared with state-of-the-art results. The statistical analysis of the results proves the ability of the algorithm to solve optimization problems.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Auction-Based Optimization Algorithm and Its Application in Rough Set Feature Selection

Jaddi

Abdullah

2021

IEEE Access

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“…Over the past few years, enormous number of metaheuristic algorithms has been used to solve optimization problems such as problems in areas of data mining (Jaddi and Abdullah, 2013a; Jaddi et al , 2013; Majdi et al , 2015; Abdolrazzagh-Nezhad and Izadpanah, 2016), pattern recognition (Kalayci et al , 2015), industry (Patil and Nataraj, 2014), engineering (Yang and Alavi, 2013; Lee and Geem, 2005; Alvankarian et al , 2015) and economics (Liang and Cuevas Juarez, 2014; Chamba and Ano, 2013). Metaheuristic algorithms can be formulated as searching for a solution optimizing a criterion between a set of candidate solutions.…”

Section: Related Workmentioning

confidence: 99%

Global search in single-solution-based metaheuristics

Jaddi

Abdullah

2020

DTA

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PurposeMetaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.Design/methodology/approachIn this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.FindingsThe proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.Originality/valueIn this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

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