Feature Selection Method Based on Grey Wolf Optimization for Coronary Artery Disease Classification

Al-Tashi, Qasem; Rais, Helmi Md; Jadid, Said

doi:10.1007/978-3-319-99007-1_25

Cited by 63 publications

(49 citation statements)

References 21 publications

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“…Therefore, a multiobjective idea of these techniques has been adapted to solve the problem of feature selection and shows a great success. GWO has recently gained much consideration for tackling the problem of feature selection in many fields such as benchmarks problems as in [12], [33], [34] , facial, voice, speech and handwriting recognition as in [35], [36], [37], EMG signal classification [38], disease diagnosis [39], [11], [40], [41], gene selection and intrusion detection systems. [42], [43].…”

Section: Related Workmentioning

confidence: 99%

“…However, these two methods are affected by the issue of premature convergence and high computational cost. Therefore, a set of heuristics and metaheuristics techniques such as Particle swarm optimization (PSO) [7], Differential Evolution (DE) [8], Ant Colony Optimization (ACO) [9], Genetic Algorithm (GA) [10], Grey Wolf Optimization (GWO) [11], [12], and Dragon algorithm (DA) [13] have been leveraged to find optimal set of features. GWO is a recently proposed metaheuristic technique influenced by the natural social intelligence of the grey wolves.…”

Section: Introductionmentioning

confidence: 99%

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Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

et al. 2020

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Feature selection or dimensionally reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multiobjective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multiobjective Grey Wolf optimizer (MOGWO) was proposed to solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGW-S is developed to optimize feature selection problems. A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method, 15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions. The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

et al. 2020

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“…Similarly, Al-Tashi et al [42] projected a multi-objective method for feature selection and reduction by employing the Wrapper based algorithm to assess the performance of selected features for classification. The Wrapper feature selection approach has been widely used in numerous applications, e.g., in the medical field for the calculation of optimum features from coronary artery disease [43]. The author [44] presented a wrapper approach for sentiment polarity classification by the integration of genetic algorithm and SVM classifier.…”

Section: Related Workmentioning

confidence: 99%

GAWA–A Feature Selection Method for Hybrid Sentiment Classification

et al. 2020

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Sentiment analysis or opinion mining is the key to natural language processing for the extraction of useful information from the text documents of numerous sources. Several different techniques, i.e., simple rule-based to lexicon-based and more sophisticated machine learning algorithms, have been widely used with different classifiers to get the factual analysis of sentiment. However, lexicon-based sentiment classification is still suffering from low accuracies, mainly due to the deficiency of domain-oriented competitive dictionaries. Similarly, machine learning-based sentiment is also tackling the accuracy constraints because of feature ambiguity from social data. One of the best ways to deal with the accuracy issue is to select the best feature-set and reduce the volume of the feature. This paper proposes a method (namely, GAWA) for feature selection by utilizing the Wrapper Approaches (WA) to select the premier features and the Genetic Algorithm (GA) to reduce the size of the premier features. The novelty of this work is the modified fitness function of heuristic GA to compute the optimal features by reducing the redundancy for better accuracy. This work aims to present a comprehensive model of hybrid sentiment by using the proposed method, GAWA. It will be valued in developing a new approach for the selection of feature-set with a better accuracy level. The experiments revealed that these techniques could reduce the feature-set upto 61.95% without negotiating the accuracy level. The new optimal feature sets enhanced the efficiency of the Naïve Bayes algorithm up to 92%. This work is compared with the conventional method of feature selection and concluded the 11% better accuracy than PCA and 8% better than PSO. Furthermore, the results are compared with the literature work and found that the proposed method outperformed the previous research.

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“…Emary et al [35] used binary grey wolf optimization (BGWO) approaches to select feature subset. Al-Tashi et al [36] introduced the feature selection method based on GWO for coronary artery disease classification. Several studies used feature selection methods based on particle swarm optimization (PSO) algorithm to search the feature space for optimal solutions [37][38][39].…”

Section: Introductionmentioning

confidence: 99%

An Improved Hybrid Feature Selection Algorithm for Electric Charge Recovery Risk

Suxiang

Shi

et al. 2020

Mathematical Problems in Engineering

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In order to extract more information that affects customer arrears behavior, the feature extraction method is used to extend the low-dimensional features to the high-dimensional features for the warning problem of user arrears risk model of electric charge recovery (ECR). However, there are many irrelevant or redundant features in data, which affect prediction accuracy. In order to reduce the dimension of the feature and improve the prediction result, an improved hybrid feature selection algorithm is proposed, integrating nonlinear inertia weight binary particle swarm optimization with shrinking encircling and exploration mechanism (NBPSOSEE) with sequential backward selection (SBS), namely, NBPSOSEE-SBS, for selecting the optimal feature subset. NBPSOSEE-SBS can not only effectively reduce the redundant or irrelevant features from the feature subset selected by NBPSOSEE but also improve the accuracy of classification. The experimental results show that the proposed NBPSOSEE-SBS can effectively reduce a large number of redundant features and stably improve the prediction results in the case of low execution time, compared with one state-of-the-art optimization algorithm, and seven well-known wrapper-based feature selection approaches for the risk prediction of ECR for power customers.

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Feature Selection Method Based on Grey Wolf Optimization for Coronary Artery Disease Classification

Cited by 63 publications

References 21 publications

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

GAWA–A Feature Selection Method for Hybrid Sentiment Classification

An Improved Hybrid Feature Selection Algorithm for Electric Charge Recovery Risk

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