Krill Herd Optimization algorithm for cancer feature selection and random forest technique for classification

Rani, R. Ranjani; Ramyachitra, D.

doi:10.1109/icsess.2017.8342875

Cited by 18 publications

(13 citation statements)

References 4 publications

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“…It was evaluated on eighteen datasets and proved its advancement over other methods. In Rani and Ramyachitra [199], the fish swarm optimization algorithm with SVM and random forest RF techniques for cancer feature selection and classification reduced only a few features from the datasets. Next, an enhanced krill herd optimization (KHO) technique was used to select the genes, and the RF technique was utilized to categorize the types of cancer.…”

Section: Krill Herd Algorithmmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

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Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.

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Section: Krill Herd Algorithmmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

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show abstract

“…A) Investigating the potential of the proposed solution algorithm: The statistical results of the obtained optimal response from different solvers such as sine cosine algorithm (SCA) 12 , krill herd optimization (KHO) algorithm 31 , GWO 26 , TLBO 27 and combination of GWO and TLBO are provided in Table 2. It is noteworthy that for all proposed algorithms, the population size is 50 and the maximum of convergence iteration is 3000, and regulation parameters for SCA and KHO solver are presented in 12 and 31 , respectively. Also, to achieve the statistical results such as the mean of the final optimal response and its standard deviation, each proposed algorithm is repeated 20 times.…”

Section: Resultsmentioning

confidence: 99%

Security-Constrained Optimal Protection Coordination for Dual-Setting Digital Directional Overcurrent Relays in the Distribution Network Including Non-Renewable/Renewable Synchronous Distributed Generation

Pirouzi¹,

Shahi²,

Zadeh³

et al. 2021

Preprint

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In this paper, the security-constrained optimal protection coordination (SCOPC) is introduced for dual setting digital directional overcurrent relay (DDOCR) in distribution network, which including renewable and non-renewable synchronous distributed generation (SDG). The SCOPC minimizes the total operation time of DDOCRs in primary and backup protection operating to achieve a fast protection coordination. Also, to improve the flexibility in DDOCRs setting, the allowable limits of A and B coefficients, pickup current (PC) and time dial setting (TDS) in both reverse and forward directions are considered as constraints. Another constraint is the Coordination Time interval (CTI). To consideration of the mentioned scheme security, the SCOPC mechanism considered the unavailability of DDOCRs due to their failure, so the stochastic method is used to modelling of this parameter. To calculate the fault current, network variables are proportional to the daily stochastic operation results of distribution network. Moreover, the proposed problem is implemented on the standard distribution networks, and then the optimal solution is obtained with hybrid algorithm of grey wolf optimization (GWO) and training and learning optimization (TLBO). The numerical results illustrate that the proposed algorithm is able to achieve a reliable and fast protection coordination that has a low standard deviation.

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“…The results obtained by BKH-FRM was compared with other ten metaheuristic algorithms and presented high accuracy among others. Rani and Ramyachitra [168] classified the cancer types using KH algorithm and random forest classifier. They modified the algorithm by using a horizontal crossover and position mutation operator and applied to ten different gene microarray cancer datasets.…”

Section: B Swarm Intelligence Based Algorithmsmentioning

confidence: 99%

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

et al. 2021

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Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

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Krill Herd Optimization algorithm for cancer feature selection and random forest technique for classification

Cited by 18 publications

References 4 publications

Multiclass feature selection with metaheuristic optimization algorithms: a review

Multiclass feature selection with metaheuristic optimization algorithms: a review

Security-Constrained Optimal Protection Coordination for Dual-Setting Digital Directional Overcurrent Relays in the Distribution Network Including Non-Renewable/Renewable Synchronous Distributed Generation

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

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