Cuckoo search optimisation for feature selection in cancer classification: a new approach

Gunavathi, C.; Premalatha, K.

doi:10.1504/ijdmb.2015.072092

Cited by 45 publications

(19 citation statements)

References 36 publications

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“…BCS has successfully applied to two datasets of theft detection in a power system with optimum path forest classifier and obtained that BCS was the very suitable and fastest approach in solving feature selection for industrial datasets. Gunavathi and Premalatha [130] used CS algorithm for classification in microarray gene data of cancer. The features were ranked according to T and F-statistics, and KNN classifier was used as a fitness function.…”

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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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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“…After random selection, N is used to indicate no selection under the feature, and Y is used to indicate selection under the feature. The values of i and j are calculated by formula (33).…”

Section: Maximum Pearson Maximum Distance Improved Whale Optimizatmentioning

confidence: 99%

“…The meta-heuristic optimization algorithm is indispensable in the wrapper algorithm [9] due to the following reasons: (i) the concept is simple and the program is easy to implement; (ii) they can break out of the local best; (iii) global optimal value can be obtained. Generic metaheuristic algorithms include Bat Algorithm (BA) [10], [11], Bacterial Foraging Optimization (BFO) [31], Cuckoo Search (CS) [24], [32], [33], Genetic Algorithm (GA) [12], [13], Particle Swarm Optimization (PSO) [14], [15], [38], Whale Optimization Algorithm (WOA) [9], [34] and Multi-Verse Optimizer (MVO) [16], [17]. Among these meta-heuristic algorithms, the WOA algorithm is a recently proposed optimization algorithm which simulates valid humpback whales natural behavior (spiral movement and bubble-net foraging) [9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Algorithm for Feature Selection Based on Whale Optimization Algorithm

et al. 2019

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Feature selection enhances classification accuracy by removing irrelevant and redundant feature. Feature selection plays an important role in data mining and pattern recognition. In this paper, we propose a hybrid feature subset selection algorithm called the maximum Pearson maximum distance improved whale optimization algorithm (MPMDIWOA). First, based on Pearson's correlation coefficient and correlation distance, a filter algorithm is proposed named maximum Pearson maximum distance (MPMD). Two parameters are proposed in MPMD to adjust the weights of the relevance and redundancy. Second, the modified whale optimization algorithm can act as a wrapper algorithm. After introducing the maximum value without change (MVWC) and the threshold, the filter algorithm and the wrapper algorithm are combined to form an algorithm called MPMDIWOA. In MPMDIWOA, the filter algorithm and wrapper algorithm are called different times according to changes in MVWC and threshold. Finally, the optimal classification accuracy was found. The proposed method is tested on 10 benchmark datasets from UCI machine learning databases. The experimental results show that the classification accuracy of the proposed algorithm is significantly higher than that of the other three wrapper algorithms and one hybrid algorithm.

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“…Finally integrating the predictions of ensemble members with majority voting. [4] applied Cuckoo search algorithm for gene identification in tumor classification task. In the first process, the top-ten cancer related genes are identified by T-Statistics, signal-to-noise ratio and F-Test.…”

Section: Literature Reviewmentioning

confidence: 99%

Composition of Feature Relevancy Based Biomarker Gene Selection in Gene Expression Dataset

Pyingkodi*,

Shanthi

2019

IJRTE

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Cancer gene selection plays a prominent work in the area of Bioinformatics. Gene selection methods aim to retain relevant genes and remove redundant genes. This proposed technique deals on gene selection techniques based on information theory. By investigating the information theory based on composition of feature relevancy, we consider that a excellent gene technique method could boost novel classification of the cancer gene data while reducing gene redundancy. Therefore, a modified gene selection technique called Composition of Feature Relevancy (CFR) is carried out. To assess CFR, the experiments are carrying out on five real-world cancer gene expression data sets and three best classifiers (KNN, Support Vector Machine and Random forest). The modified gene selection technique gives best outcome when competing to other recent technique in terms of accuracy and sensitivity in classification.

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Cuckoo search optimisation for feature selection in cancer classification: a new approach

Cited by 45 publications

References 36 publications

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

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

A Novel Hybrid Algorithm for Feature Selection Based on Whale Optimization Algorithm

Composition of Feature Relevancy Based Biomarker Gene Selection in Gene Expression Dataset

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