Hybrid filter-wrapper feature selection using whale optimization algorithm: A multi-objective approach

Got, Adel; Moussaoui, Abdelouahab; Zouache, Djaafar

doi:10.1016/j.eswa.2021.115312

Cited by 74 publications

(33 citation statements)

References 36 publications

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“…In the second phase of our experiment, three multiobjective strategies, MOPSO [60], BMOGWO-S [61], and FW-GPAWOA [46], are employed as benchmark approaches in the comparative analysis to confirm the efficiency of the suggested EBMOChOA-FW approach. The MOPSO method is a multi-objective PSO-based technique where a supplementary archive is integrated to save non-dominated options, and an archive controller with an adaptive grid technique is utilised to boost the convergence and variation of the population.…”

Section: B Methods For Comparison and Parameter Settingsmentioning

confidence: 99%

“…To this end, four different multi-objective filter-based FS techniques were developed, each using MI and gain ratio based entropy as filter assessment measurements. Using the whale optimization technique (WOA), Got et al [46] suggested a new hybrid filter-wrapper FS solution in 2021. It is a multi-objective technique suggested that optimises both filter and wrapper fitness concurrently.…”

Section: Related Workmentioning

confidence: 99%

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An Enhanced Binary Multiobjective Hybrid Filter-Wrapper Chimp Optimization Based Feature Selection Method for COVID-19 Patient Health Prediction

et al. 2022

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This work aims to discover the relevant factors to predict the health condition of COVID-19 patients by employing a fresh and enhanced binary multi-objective hybrid filter-wrapper chimp optimization (EBMOChOA-FW) based feature selection (FS) approach. FS is a preprocessing approach that has been highly fruitful in medical applications, as it not only reduces dimensionality but also allows us to understand the origins of an illness. Wrappers are computationally expensive but have excellent classification performance, whereas filters are recognized as quick techniques, although they are less accurate. This study presents an advanced binary multi-objective chimp optimization method based on the hybridization of filter and wrapper for the FS task using two archives. In exceptional instances, the initial ChOA version becomes stuck at the local optima. As a result, a novel ChOA termed EBMOChOA is developed here by integrating the Harris Hawk Optimization (HHO) into the original ChOA to improve the optimizer's search capabilities and broaden the usage sectors. The location change step in the ChOA optimizer is separated into three parts: modifying the population using HHO to produce an HHO-based population; creating hybrid entities according to HHO-based and ChOA-based individuals; and altering the search agent in the light of greedy technique and ChOA's tools. The effectiveness of the EBMOChOA-FW is proven by comparing it to five other well-known algorithms on nine different benchmark datasets. Then its strengths are applied to three real-world COVID-19 datasets to predict the health condition of COVID-19 patients.

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Section: B Methods For Comparison and Parameter Settingsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Enhanced Binary Multiobjective Hybrid Filter-Wrapper Chimp Optimization Based Feature Selection Method for COVID-19 Patient Health Prediction

et al. 2022

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“…In normal graph clustering‐based ant colony, features are divided into clusters and are presented by graphs and then the ant colony tries to find out the best possible subset. In Got et al 33 a two‐stage ant colony algorithm is proposed for selecting the best subset of features in a high dimensional data set. Regarding the time complexity of ant colony algorithm, this study tries to control the size of potential features for ant movements.…”

Section: Related Workmentioning

confidence: 99%

“…Also, this study defines the accuracy of KNN and distance measurement as the objectives of the fitness function. Moreover, in Got et al 33 a novel feature selection approach based on WOA is proposed. In this study, an ensemble of filter and wrapper techniques is presented.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective whale optimization algorithm‐based feature selection for intelligent systems

Riyahi

Rafsanjani

Gupta

et al. 2022

Int J of Intelligent Sys

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With regard to large dimensions of contemporary data sets and restricted computational time of intelligent systems, reducing the dimensions of data sets is necessary. Feature selection is a practical way to remove a set of redundant, irrelevant, and noisy features. In this way, the speed of decision-making procedure will be increased while the accuracy of decisions will be retained. To this end, numerous attentions have been attracted to the topic and consequently, extensive range of methods has been proposed. Regarding the goals of the feature selection concept, the proposed algorithms in this field must be fast and accurate. Therefore, this paper proposes a light meanwhile accurate algorithm to fulfill the mentioned goals. The presented algorithm takes the speed advantage of Whale Optimization Algorithm (WOA) to propose a novel feature selection method for intelligent systems. Moreover, to reach the goal of accuracy, the proposed strategy considers three important fitness objectives, namely, the number of selected features, the accuracy of classification, and information gain. The proposed scheme considers an accurate multiobjective fitness function instead of manipulating the basic algorithm. The reason is that improving the basic algorithms, WOA in our case, may lead to loading

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“…Evolutionary computation (EC) technology is well known for their global search ability [7]. The most commonly used EC technologies in feature selection include Genetic Algorithm (GA) [8], Particle Swarm Optimization(PSO) [9][10][11][12], Grey Wolf Optimization(GWO) [13,14], Artificial Bee Colony(ABC) [3,15], Whale Optimization Algorithm(WOA) [16,17] and Salp Swarm Algorithm(SSA) [18,19].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Harris Hawks Optimization With Associative Learning and Chaotic Local Search for Feature Selection

Zhang

et al. 2022

IEEE Access

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In the classification problem, datasets often have a large number of features, but not all features are useful for classification. A lot of irrelevant features may even reduce the performance. Feature selection is to remove irrelevant features by minimizing the number of the feature subset and minimizing the classification error rate.So it can be regarded as a multi-objective optimization problem. Beacuse of its simple structure and easy implementation, Harris Hawks Optimization algorithm(HHO) is widely employed in optimization problems.In this paper, the multi-objective HHO is applied to address the feature selection problem. In order to improve the search ability of the algorithm, associative learning, grey wolf optimization and chaotic local search are introduced into it. An external repository is used to save non-dominant solution set. The results of feature selection on the sixteen University of CaliforniaIrvine (UCI) datasets show that the proposed method can effectively remove redundant features and improve the classification performance of the algorithm.INDEX TERMS multi-objective harris hawks optimization; feature selection; associative learning;chaotic local search

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Hybrid filter-wrapper feature selection using whale optimization algorithm: A multi-objective approach

Cited by 74 publications

References 36 publications

An Enhanced Binary Multiobjective Hybrid Filter-Wrapper Chimp Optimization Based Feature Selection Method for COVID-19 Patient Health Prediction

An Enhanced Binary Multiobjective Hybrid Filter-Wrapper Chimp Optimization Based Feature Selection Method for COVID-19 Patient Health Prediction

Multiobjective whale optimization algorithm‐based feature selection for intelligent systems

Multiobjective Harris Hawks Optimization With Associative Learning and Chaotic Local Search for Feature Selection

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