Prediction of Gene Selection Features Using Improved Multi-objective Spotted Hyena Optimization Algorithm

Divya, S.; Kiran, Eranki L. N.; Rao, Madhu Sudana; Vemulapati, Pujitha

doi:10.1007/978-981-15-0132-6_5

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…Rather than engineering problems, SHO is also castoff to resolve numerous mechanical and electrical glitches such as reduction of load for brake constituents belonging to automobiles, PID factor optimization for AVR systems, soil strength estimation, economic load dispatch problem, and improvement in software project quality also [ 37 – 41 ]. The same algorithm has also been used with neural networks, to solve feature selection and classification problems [ 42 – 46 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Approach of Spotted Hyena Optimization Integrated with Quadratic Approximation for Training Wavelet Neural Network

2022

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Spotted hyena optimization (SHO) is one of the newly evolved swarm-based metaheuristic optimization methods based on the social life cycle of hyenas. In recent times SHO is being applied to various engineering applications as well as to solve real-life complications. In this paper, we have hybridized SHO with quadratic approximation operator (QAO), termed as QASHO. The proposed QASHO has been scrutinized to enhance the exploitation ability, aiming to achieve global optimum, as QAO performs better within the local confinement region. Furthermore, the proposed approach shows improved strength in terms of escaping from the local minima trap, as in each iteration we discard some of the worst individuals by some suitable ones. To validate the proficiency of the proposed QASHO approach, 28 standard problems have been preferred in connection with IEEE-CEC-2017. The outcome observed from the suggested method has also equated with contemporary metaheuristic approaches. To prove the statistical significance, a nonparametric test has also been accomplished. Additionally as a real-life application, the suggested approach QASHO has utilized to train wavelet higher-order neural networks (HONN) by choosing datasets from the UCI store. The above correlations reveal that QASHO can deal with complex optimization tasks.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Approach of Spotted Hyena Optimization Integrated with Quadratic Approximation for Training Wavelet Neural Network

2022

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“…In [66], a hybrid solution was presented based on Fisher-score and MOFOA in which the concepts of repository and binary tournament selection are used to solve the problem of multi-objective gene selection. Divya et al [37] have proposed a hybrid solution based on IG filter and MOSHO considering the accuracy of SVM and the number of the selected genes. Some other hybrid multi-objective methods include NSGA-II [31][32][33][34], MOPSO [35,39,67], MOGA [36], MOSHO [37,38], MOSSO [39], MOACO [40], MOBAT [68,69], and so on.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Divya et al [37] have proposed a hybrid solution based on IG filter and MOSHO considering the accuracy of SVM and the number of the selected genes. Some other hybrid multi-objective methods include NSGA-II [31][32][33][34], MOPSO [35,39,67], MOGA [36], MOSHO [37,38], MOSSO [39], MOACO [40], MOBAT [68,69], and so on.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Additionally, hybrid multi-objective methods have been considered by many researchers in recent years for the simultaneously optimizing the objectives of minimizing the number of genes and maximizing the efficiency of the classification model. Some of these methods are non-dominated sorting GA II (NSGA-II) [35][36][37][38], MOPSO [39], MOGA [40], multi-objective spotted hyena optimizer (MOSHO) [41,42], multi-objective simplified swarm optimization (MOSSO) [10], MOACO [43], and so on.…”

Section: Introductionmentioning

confidence: 99%

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A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data

Nouri-Moghaddam

Ghazanfari

Fathian

2021

Neural Comput & Applic

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Microarray technology is known as one of the most important tools for collecting DNA expression data. This technology allows researchers to investigate and examine types of diseases and their origins. However, microarray data are often associated with a small sample size, a significant number of genes, imbalanced data, etc., making classification models inefficient. Thus, a new hybrid solution based on a multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is presented to solve the gene selection problem and construct the Ensemble Classifier. In the proposed solution, a multi-filter model (i.e., ensemble filter) is proposed as preprocessing step to reduce the dataset's dimensions, using a combination of five filter methods to remove redundant and irrelevant genes. Accordingly, the results of the five filter methods are combined using a voting-based function. Additionally, the results of the proposed multi-filter indicate that it has good capability in reducing the gene subset size and selecting relevant genes. Then, an AC-MOFOA based on the concepts of non-dominated sorting, crowding distance, chaos theory, and adaptive operators is presented. AC-MOFOA as a wrapper method aimed at reducing dataset dimensions, optimizing KELM, and increasing the accuracy of the classification, simultaneously. Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data. The performance of the proposed algorithm was evaluated on nine public microarray datasets, and its results were compared in terms of the number of selected genes, classification efficiency, execution time, time complexity, hypervolume indicator, and spacing metric with five hybrid multi-objective methods, and three hybrid single-objective methods. According to the results, the proposed hybrid method could increase the accuracy of the KELM in most datasets by reducing the dataset's dimensions and achieve similar or superior performance compared to other multi-objective methods. Furthermore, the proposed Ensemble Classifier model could provide better classification accuracy and generalizability in the seven of nine microarray datasets compared to conventional ensemble methods. Moreover, the comparison results of the Ensemble Classifier model with three state-of-the-art ensemble generation methods indicate its competitive performance in which the proposed ensemble model achieved better results in the five of nine datasets. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-021-06459-9.

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“…Recently, Ref. [29] implements an SHO version able to deal with multi-objective problems for the prediction of characteristics in gene selection through its combination with machine learning algorithms like SVM.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes

et al. 2020

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In this study, a system for faults detection using a combination of Support Vector Data Description (SVDD) with metaheuristic algorithms is presented. The presented approach is applied to a real industrial process where the set of measured faults is scarce. The original contribution in this work is the industrial context of application and the comparison of swarm intelligence algorithms to optimize the SVDD hyper-parameters. Four recent metaheuristics are compared hereby to solve the corresponding optimization problem in an efficient manner. These optimization techniques are then implemented for fault detection in a multivariate industrial process with non-balanced data. The obtained numerical results seem to be promising when the considered optimization techniques are combined with SVDD. In particular, the Spotted Hyena algorithm outperforms other metaheuristics reaching values of F1 score near 100% in fault detection.

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Prediction of Gene Selection Features Using Improved Multi-objective Spotted Hyena Optimization Algorithm

Cited by 5 publications

References 12 publications

A Hybrid Approach of Spotted Hyena Optimization Integrated with Quadratic Approximation for Training Wavelet Neural Network

A Hybrid Approach of Spotted Hyena Optimization Integrated with Quadratic Approximation for Training Wavelet Neural Network

A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data

Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes

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