A novel hybrid feature selection method for microarray data analysis

Lee, Chien-Pang; Leu, Yungho

doi:10.1016/j.asoc.2009.11.010

Cited by 160 publications

(56 citation statements)

References 26 publications

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“…They mainly focus on achieving the best possible accuracy performance with a similar time complexity of Filter techniques. In these methods, the selection of optimal subset of features is usually built inside the classifier, and they iteratively use classifier parameters to select subsets of features [10], [11].…”

Section: Gene Selection Methods In Cancer Classificationmentioning

confidence: 99%

The Performance of Bio-Inspired Evolutionary Gene Selection Methods for Cancer Classification Using Microarray Dataset

Alshamlan¹,

Badr²,

Al-Ohali³

2014

IJBBB

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Abstract-Microarray based gene expression profiling has become an important and promising dataset for cancer classification that are used for diagnosis and prognosis purposes. It is important to determine the informative genes that cause the cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. Furthermore, find accurate gene selection method that reduce the dimensionality and select informative genes is very significant issue in cancer classification area. In literature, there are several gene selection methods for cancer classification using microarray dataset. However, most of them did not concern on identifying minimum number of informative genes with high classification accuracy. Therefore, in our research study we discuss the performance of Bio-Inspired evolutionary gene selection method in cancer classification using microarray dataset. And, we prove that the Bio-Inspired evolutionary gene selection methods have superior classification accuracy with minimum number of selected genes.

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Section: Gene Selection Methods In Cancer Classificationmentioning

confidence: 99%

The Performance of Bio-Inspired Evolutionary Gene Selection Methods for Cancer Classification Using Microarray Dataset

Alshamlan¹,

Badr²,

Al-Ohali³

2014

IJBBB

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“…The problem of extracting significant knowledge from microarray data requires the development of robust methods able to address this task [9,12]. Lu and Han [11] emphasize that the most important aspects of classification and gene selection methods are their computation time, classification accuracy and ability to reveal biologically meaningful gene information.…”

Section: Introductionmentioning

confidence: 99%

A New Gene Selection Method Based on Random Subspace Ensemble for Microarray Cancer Classification

Armano

Chira

Hatami

2011

Pattern Recognition in Bioinformatics

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Abstract. Gene expression microarray data provides simultaneous activity measurement of thousands of features facilitating a potential effective and reliable cancer diagnosis. An important and challenging task in microarray analysis refers to selecting the most relevant and significant genes for data (cancer) classification. A random subspace ensemble based method is proposed to address feature selection in gene expression cancer diagnosis. The introduced Diverse Accurate Feature Selection method relies on multiple individual classifiers built based on random feature subspaces. Each feature is assigned a score computed based on the pairwise diversity among individual classifiers and the ratio between individual and ensemble accuracies. This triggers the creation of a ranked list of features for which a final classifier is applied with an increased performance using minimum possible number of genes. Experimental results focus on the problem of gene expression cancer diagnosis based on microarray datasets publicly available. Numerical results show that the proposed method is competitive with related models from literature.

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“…These datasets were selected to justify the use of this kernel model in the classification of high dimensionality problems. Furthermore, other soft computing techniques have been implemented to address this problem [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Fernández-Navarro

Hervás-Martı́nez

Ruíz

et al. 2012

Applied Soft Computing

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A novel hybrid feature selection method for microarray data analysis

Cited by 160 publications

References 26 publications

The Performance of Bio-Inspired Evolutionary Gene Selection Methods for Cancer Classification Using Microarray Dataset

The Performance of Bio-Inspired Evolutionary Gene Selection Methods for Cancer Classification Using Microarray Dataset

A New Gene Selection Method Based on Random Subspace Ensemble for Microarray Cancer Classification

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

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