A novel aggregate gene selection method for microarray data classification

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

doi:10.1016/j.patrec.2015.03.018

Cited by 53 publications

(27 citation statements)

References 24 publications

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“…Both researchers showed similar results, but our study has a higher result. Thanh Nguyen et al [22], used the MAPH method for feature selection from cancer dataset and the classifier PNN (probabilistic neural network) used for cancer classification. The author gave an unclear explanation about how many features selected from colon cancer dataset.…”

Section: No Of Experimentsmentioning

confidence: 99%

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Feature selection from colon cancer dataset for cancer classification using Artificial Neural Network

Rahman¹,

Muniyandi²

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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In the fast-growing field of medicine and its dynamic demand in research, a study that proves significant improvement to healthcare seems imperative especially when it is on cancer research. This research paved the way for such significant findings by the inclusion of feature selection as one of its major components. Feature selection has become a vital task to apply data mining algorithms effectively in the real-world problems for classification. The Feature selection has been the focus of interest for quite some time and much-completed work related to it. This study used feature selection for improving classification accuracy on the cancerous dataset. This study proposed Artificial Neural Network (ANN) for cancer classification by the feature selection on colon cancer dataset. The study used the best first search method in Weka tools for feature selection. The result of the experiment achieved 98.4 %, accuracy for cancer classification after feature selection by using the proposed algorithm. The result indicated that feature selection improves the classification accuracy based on the experiment conducted on the colon cancer dataset.

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Section: No Of Experimentsmentioning

confidence: 99%

“…Thanh Nguyen et al [22] introduced a new technique in the selection of features based on a modification of the analytic hierarchy process (MAHP). The author used different classifiers covering linear discriminant analysis, probabilistic neural network, k-nearest neighbors, multilayer perceptron, and support vector machine.…”

Section: Introductionmentioning

confidence: 99%

Feature selection from colon cancer dataset for cancer classification using Artificial Neural Network

Rahman¹,

Muniyandi²

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…If we can make full use of available human expression profiles and realize repeatable diagnoses, there is no doubt that it will bring great convenience to cancer patients. The analysis of microarray gene expression data [ 3 ] and protein expression data can be used to grasp the information of physiological activities at the molecular level, which is widely used in the field of biomedicine. However, a large number of irrelevant and redundant values exist in expression profiles.…”

Section: Introductionmentioning

confidence: 99%

Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony

Gao

2017

Molecules

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Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM. The proposed PA-SVM method is applied to nine cancer datasets, including five datasets of outcome prediction and a protein dataset of ovarian cancer. By comparison with other classification methods, the results demonstrate the effectiveness and the robustness of the proposed PA-SVM method in handling various types of data for cancer classification.

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“…LLE and its extensions are a promising technique that can be used to solve the dimension reduction problem of high-dimensional data (Roweis and Saul, 2000). To evaluate gene selection methods, in addition to the predictive ability of gene subsets, two other important aspects that must be considered include stability of the selected genes and computational costs (Nguyen et al, 2015). Gene subsets with low dimensionality and high classification ability can be selected from gene expression profiles.…”

Section: Introductionmentioning

confidence: 99%

Locally linear embedding and neighborhood rough set-based gene selection for gene expression data classification

Sun

Wang

et al. 2016

Genet. Mol. Res.

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ABSTRACT. Cancer subtype recognition and feature selection are important problems in the diagnosis and treatment of tumors. Here, we propose a novel gene selection approach applied to gene expression data classification. First, two classical feature reduction methods including locally linear embedding (LLE) and rough set (RS) are summarized. The advantages and disadvantages of these algorithms were analyzed and an optimized model for tumor gene selection was developed based on LLE and neighborhood RS (NRS). Bhattacharyya distance was introduced to delete irrelevant genes, pair-wise redundant analysis was performed to remove strongly correlated genes, and the wavelet soft threshold was determined to eliminate noise in the gene datasets. Next, prior optimized search processing was carried out. A new approach combining dimension reduction of LLE and feature reduction of NRS (LLE-NRS) was developed for selecting gene subsets, and then an open source software Weka was applied to distinguish different tumor types and verify the cross-validation classification accuracy of our proposed method. The experimental results demonstrated that the classification performance of the proposed LLE-NRS for selecting gene subset outperforms those of other related models in terms of accuracy, and our proposed approach is feasible and effective in the field of highdimensional tumor classification.

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A novel aggregate gene selection method for microarray data classification

Cited by 53 publications

References 24 publications

Feature selection from colon cancer dataset for cancer classification using Artificial Neural Network

Feature selection from colon cancer dataset for cancer classification using Artificial Neural Network

Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony

Locally linear embedding and neighborhood rough set-based gene selection for gene expression data classification

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