Microarray Cancer Gene Feature Selection Using Spider Monkey Optimization Algorithm and Cancer Classification using SVM

Rani, R. Ranjani; Ramyachitra, D.

doi:10.1016/j.procs.2018.10.358

Cited by 31 publications

(15 citation statements)

References 9 publications

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“…R. RanjaniRani et al [8] Proposed a combination of Spider Monkey Optimization Algorithm along with the Support Vector Machine classification algorithm and employed for the microarray cancer gene expression data. It has two phases: first is to eliminate irrelevant and redundant genes and select the subset of genes from the large volume of genes using the Spider Monkey Optimization algorithm.…”

Section: Related Workmentioning

confidence: 99%

The Hybrid Machine Learning Model Based on Random Forest Optimized by PSO and ACO for Predicting Heart Disease

Khourdifi¹,

Bahaj²

2019

Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 20

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In this paper, we used the hybrid Machine Learning model, for proposed PA-RF, a classification based on Random Forest model, optimized by Particle Swarm Optimization (PSO) associated with Ant Colony Optimization (ACO), and we use Fast Correlation-Based Feature Selection (FCBF) method to filter redundant and irrelevant characteristics, in order to improve the quality of heart disease classification. The proposed mixed approach is applied to the heart disease dataset. The results demonstrate the effectiveness and robustness of the proposed hybrid method in processing various types of data for the classification of heart disease. Therefore, this study examines the different automatic learning algorithms and compares the results using different performance measures, i.e. Accuracy, Precision, Recall, F1-Score, etc. The data set used in this study comes from the UCI's automatic learning repository, entitled "Heart Disease" Data set. We can be concluded that PA-RF has demonstrated efficiency and robustness compared to other classification methods.

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Section: Related Workmentioning

confidence: 99%

The Hybrid Machine Learning Model Based on Random Forest Optimized by PSO and ACO for Predicting Heart Disease

Khourdifi¹,

Bahaj²

2019

Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 20

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“…Spider Monkey Optimization algorithm [11] is used to identify the number of genes in cancer data. Experimental performed with various benchmark cancer datasets reveals that it outperforms other methods with the minimum amount of genes and maximum classification accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Naïve Bayes guided Binary Firefly Algorithm for Gene Selection in Cancer Classification

Rajalaxmi*,

Gothai,

Thamilselvan

et al. 2019

IJRTE

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The bioinformatics research must deal with the analysis of the large volume of data. Disease classification deals with the identification of relevant genes in almost all gene expression analyses, where researchers attempt to select a minimum number of genes with exceptional performance. The gene selection process mainly selects significant genes related to the disease. This work aims to accomplish relevant genes from large volume of candidate genes that help to identify cancers. In the proposed work, Binary Firefly Algorithm (BFA) helps to identify related genes using the Naive Bayes classifier. Based on the experimental results, Naïve Bayes guided Binary Firefly Algorithm (NBBFA) provided high accuracy with fewer genes

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“…In order to continue or stop the process, a stopping criterion is tested. The best subset final of attributes with large accuracy of classification is defining as the better optimal results [26].…”

Section: Related Workmentioning

confidence: 99%

“…Feature selection problems is a real word problem in order to solve these problems, the researchers worked to find approaches can solve these problems well during get better performance from used algorithms mimics the inbred behavior of the animal in nature when it searches about the food [8,26]. Therefore, these techniques are used for selecting the features that are depending on the workspace search that generates suitable and optimal feature subsets [41], [42], [34].…”

Section: Introductionmentioning

confidence: 99%

Optimization Algorithms to Solve Feature Selection Problem: A Review

Abualigah¹

2019

IJSAIT

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Feature Selection (FS) method is one of the most important data pre-processing steps in data mining domain, it is used to find the essential features subset in order to make a new subset of informative features. The model that used the informative subset such that a classification model built only with this subset would get better predictive accuracy than the model that used a complete set of features. In this paper, we provide a summary of almost all of the methods present in the literature of 2018. The goal is to provide a general presentation to variant optimization algorithms that can be applied to a wide array of machine learning problems. We converge on Filter, Wrapper and Embedded methods. Finally, we analyzed the results of several feature selection techniques on standard datasets to illustrate the applicability of feature selection techniques.

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Microarray Cancer Gene Feature Selection Using Spider Monkey Optimization Algorithm and Cancer Classification using SVM

Cited by 31 publications

References 9 publications

The Hybrid Machine Learning Model Based on Random Forest Optimized by PSO and ACO for Predicting Heart Disease

The Hybrid Machine Learning Model Based on Random Forest Optimized by PSO and ACO for Predicting Heart Disease

Naïve Bayes guided Binary Firefly Algorithm for Gene Selection in Cancer Classification

Optimization Algorithms to Solve Feature Selection Problem: A Review

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