In many real-world problems such as gene selection which is a high dimensional problem, the large number of features is the main challenge. Exhaustive search to find the optimal feature subset is not feasible in a rea...
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(纸本)9781538668788
In many real-world problems such as gene selection which is a high dimensional problem, the large number of features is the main challenge. Exhaustive search to find the optimal feature subset is not feasible in a reasonable time and might reduce the performance of the classifier. To address this issue, many binary metaheuristic algorithms are proposed to approximate the optimal solution by removing irrelevant features within an acceptable computational time. This paper presents a comparative analysis to evaluate the efficiency of transfer function-based binary metaheuristic algorithms for feature selection. We compare the performance of popular algorithmsbinary Bat Algorithm (BBA), binary Gravitational Search Algorithm (BGSA) and binary Grey Wolf Optimization (bGWO) with different transfer functions. The experimental results on seven well-known datasets including high dimensional datasets Colon and Leukemia demonstrate that BGSA is an efficient and suitable algorithm for feature selection from both kinds of datasets with low and high dimensions.
binary metaheuristic algorithms prove to be invaluable for solving binary optimization problems. This paper proposes a binary variant of the peacock algorithm (PA) for feature selection. PA, a recent metaheuristic alg...
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binary metaheuristic algorithms prove to be invaluable for solving binary optimization problems. This paper proposes a binary variant of the peacock algorithm (PA) for feature selection. PA, a recent metaheuristic algorithm, is built upon lekking and mating behaviors of peacocks and peahens. While designing the binary variant, two major shortcomings of PA (lek formation and offspring generation) were identified and addressed. Eight binary variants of PA are also proposed and compared over mean fitness to identify the best variant, called binary peacock algorithm (bPA). To validate bPA's performance experiments are conducted using 34 benchmark datasets and results are compared with eight well-known binary metaheuristic algorithms. The results show that bPA classifies 30 datasets with highest accuracy and extracts minimum features in 32 datasets, achieving up to 99.80% reduction in the feature subset size in the dataset with maximum features. bPA attained rank 1 in Friedman rank test over all parameters.
Feature Subset Selection(FSS)is an NP-hard problem to remove redundant and irrelevant features particularly from medical data,and it can be effectively addressed by metaheuristic ***,existing binary versions of metahe...
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Feature Subset Selection(FSS)is an NP-hard problem to remove redundant and irrelevant features particularly from medical data,and it can be effectively addressed by metaheuristic ***,existing binary versions of metaheuristicalgorithms have issues with convergence and lack an effective binarization method,resulting in suboptimal solutions that hinder diagnosis and prediction *** paper aims to propose an Improved binary Quantum-based Avian Navigation Optimizer Algorithm(IBQANA)for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic *** proposed IBQANA’s contributions include the Hybrid binary Operator(HBO)and the Distance-based binary Search Strategy(DBSS).HBO is designed to convert continuous values into binary solutions,even for values outside the[0,1]range,ensuring accurate binary *** the other hand,DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates *** combining exploration and exploitation phases based on an adaptive probability function,DBSS effectively avoids local *** effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets,with feature numbers ranging from 8 to 10,***'s effectiveness is evaluated regarding the accuracy,fitness,and selected features and compared with seven binarymetaheuristic ***,IBQANA is utilized to detect *** results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical *** proposed method presents a promising solution to the FSS problem in medical data preprocessing.
Since most metaheuristicalgorithms for continuous search space have been developed, a number of transfer functions have been proposed including S-shaped, V-shaped, linear, U-shaped, and X-shaped to convert the contin...
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Since most metaheuristicalgorithms for continuous search space have been developed, a number of transfer functions have been proposed including S-shaped, V-shaped, linear, U-shaped, and X-shaped to convert the continuous solution to the binary one. However, most existing transfer functions do not provide exploration and exploitation required to solve complex discrete problems. Thus, in this study, an improved binary GWO named extremum-based GWO (BE-GWO) algorithm is introduced. The proposed algorithm proposes a new cosine transfer function (CTF) to convert the continuous GWO to the binary form and then introduces an extremum (Ex) search strategy to improve the efficiency of converted binary solutions. The performance of the BE-GWO was evaluated through solving two binary optimization problems, the feature selection and the 0-1 multidimensional knapsack problem (MKP). The results of feature selection problems were compared with several well-known binary metaheuristic algorithms such as BPSO, BGSA, BitABC, bALO, bGWO, BDA, BSSA, and BinABC. Moreover, the results were compared with four versions of the binary GWO, the binary PSO, and the binary ABC. In addition, the BE-GWO algorithm was evaluated to solve the 0-1 MKP with difficult and very difficult benchmark instances and the results were compared with several binary GWO variants. The results of two binary problems were statistically analyzed by the Friedman test. The experimental results showed that the proposed BE-GWO algorithm enhances the performance of binary GWO in terms of solution accuracy, convergence speed, exploration, and balancing between exploration and exploitation.& COPY;2023 Elsevier B.V. All rights reserved.
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