A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization

Sharbaf, Fatemeh Vafaee; Mosafer, Sara; Moattar, Mohammad Hossein

doi:10.1016/j.ygeno.2016.05.001

Cited by 117 publications

(27 citation statements)

References 22 publications

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“…Conversely, wrapper methods have better performance; however, they require great computational expenses [11] . Therefore, numerous hybrid methods have been proposed to achieve optimal performance [12] , [13] , [14] , [15] , [16] .…”

Section: Introductionmentioning

confidence: 99%

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification

Gao

et al. 2017

Genomics, Proteomics &Amp; Bioinformatics

108

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It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine (IG-SVM) in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further removal of redundant genes was performed using SVM to eliminate the noise in the datasets more effectively. Finally, the informative genes selected by IG-SVM served as the input for the LIBSVM classifier. Compared to other related algorithms, IG-SVM showed the highest classification accuracy and superior performance as evaluated using five cancer gene expression datasets based on a few selected genes. As an example, IG-SVM achieved a classification accuracy of 90.32% for colon cancer, which is difficult to be accurately classified, only based on three genes including CSRP1, MYL9, and GUCA2B.

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Section: Introductionmentioning

confidence: 99%

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification

Gao

et al. 2017

Genomics, Proteomics &Amp; Bioinformatics

108

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“…Classification [47] and feature selection [48–50] are widely applied in bioinformatics applications such as gene selection [51, 52] and gene expression [53–55]. Chinnaswamy A [56] proposed a hybrid feature selection using correlation coefficients and particle swarm optimization on microarray gene expression data.…”

Section: Methodsmentioning

confidence: 99%

CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests

Fan

2017

BMC Bioinformatics

183

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BackgroundThe random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization.ResultsWe propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability.ConclusionThe training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.

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“…The research focused on how to optimize the feature selection algorithm. Some used the addition of optimization algorithm [16][17][18][19][20], i.e., genetic algorithm or particle swarm optimization, while some used the fuzzy approach [21][22][23][24][25] Information 2020, 11, 38 2 of 16 and, most recently, the ensemble approach [17,21,[26][27][28][29][30][31][32][33]. In general, a conventional feature selection is quite unstable when faced with changing data characteristics.…”

Section: Introductionmentioning

confidence: 99%

Optimal Feature Aggregation and Combination for Two-Dimensional Ensemble Feature Selection

Alhamidi

Jatmiko

2020

Information

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Feature selection is a way of reducing the features of data such that, when the classification algorithm runs, it produces better accuracy. In general, conventional feature selection is quite unstable when faced with changing data characteristics. It would be inefficient to implement individual feature selection in some cases. Ensemble feature selection exists to overcome this problem. However, with the advantages of ensemble feature selection, some issues like stability, threshold, and feature aggregation still need to be overcome. We propose a new framework to deal with stability and feature aggregation. We also used an automatic threshold to see whether it was efficient or not; the results showed that the proposed method always produces the best performance in both accuracy and feature reduction. The accuracy comparison between the proposed method and other methods was 0.5–14% and reduced more features than other methods by 50%. The stability of the proposed method was also excellent, with an average of 0.9. However, when we applied the automatic threshold, there was no beneficial improvement compared to without an automatic threshold. Overall, the proposed method presented excellent performance compared to previous work and standard ReliefF.

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A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization

Cited by 117 publications

References 22 publications

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification

CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests

Optimal Feature Aggregation and Combination for Two-Dimensional Ensemble Feature Selection

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