A robust multiobjective Harris’ Hawks Optimization algorithm for the binary classification problem

Dökeroğlu, Tansel; Deniz, Ayça; Kızılöz, Hakan Ezgi

doi:10.1016/j.knosys.2021.107219

Cited by 32 publications

(26 citation statements)

References 63 publications

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“…Likewise, in [125], Chellal and Benmessahed developed a binary version of the HHO in order to be able to have an accurate detection of a protein complex. Dokeroglu et al [126] developed a binary version of multiobjective HHO to be able to solve a classification problem. They introduced novel discrete besiege (exploitation) and perching (exploration) operators.…”

Section: Binary Hhomentioning

confidence: 99%

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Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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Section: Binary Hhomentioning

confidence: 99%

“…The novel algorithm, MOBHHO, was applied on microarray data gene selection by using two fitness functions: SVM and KNN. Another binary multiobjective version was proposed in [126].…”

Section: Multiobject Hhomentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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“…SCHHO shows promising performance on the sixteen datasets with low and high-dimensions exceeding 15000 attributes. Dokeroglu et al [45] proposed a new multiobjective HHO algorithm for the solution of the wellknown binary classification problem in which a new discrete exploration (perching) and exploitation (besiege) operators for the hunting patterns of the hawks is developed. Moreover, it is applied to a real-world dataset, Coronavirus disease (COVID19) dataset.…”

Section: ) Multi-objective Evolutionary Computation-based Approachesmentioning

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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“…The same does not happen with the feature subset; thus, the relationship between model performance and feature subset is nonlinear. To overcome these natural challenges, feature selection needs to optimize two different objectives as its focus is to decrease the total amount of features while enhancing the performance of the model [46]. The task of feature selection is defined as…”

Section: Feature Selection (Fs)mentioning

confidence: 99%

“…By considering these facts for feature selection problem, an ideal solution is to use a single feature that can separate the classes perfectly. Figure 2 [46] represents the sample solution for feature selection. Different sample solutions fs1, fs2, fs3, fs4, fs5, and fs6 are provided.…”

Section: Feature Selection (Fs)mentioning

confidence: 99%

An Optimized Feature Selection Method Using Ensemble Classifiers in Software Defect Prediction for Healthcare Systems

Mohammad

Imtiaz

Shakya

et al. 2022

Wireless Communications and Mobile Computing

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The healthcare systems are extensively being used with increased focus on safety of patients. Software engineering for healthcare applications is an emerging research area. Detecting defects is a critical step of software development process of healthcare applications. The performance of the Software Defect Prediction model (SDP) depends on the features of healthcare system; irrelevant features decrease the performance of the model. An optimized feature selection technique is needed to recognize and remove the irrelevant features. In this study, a new optimized feature selection technique, i.e., multiobjective Harris Hawk Optimization (HHO), is proposed for binary classification problem with Adaptive Synthetic Sampling (ADASYN) Technique. Multiobjective HHO is proposed with two main objectives, one to reduce the total amount of selected features and the other to maximize the performance of the proposed model. The multiobjective feature selection technique helps to find the optimal solution to achieve the desired objectives and increase the classification performance in terms of accuracy, AUC, precision, recall, and F1-measure. The study conducts an experiment on a healthcare dataset. Six different search techniques (RF, SVM, bagging, adaptive boosting, voting, and stacking) are implemented on the dataset. The proposed model helps to predict the software defects with a significant classification accuracy of 0.990 and AUC score of 0.992.

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A robust multiobjective Harris’ Hawks Optimization algorithm for the binary classification problem

Cited by 32 publications

References 63 publications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

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

An Optimized Feature Selection Method Using Ensemble Classifiers in Software Defect Prediction for Healthcare Systems

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