Improving the Performance of Principal Components for Classification of Gene Expression Data Through Feature Selection

2006 ◽  
pp. 325-332
Author(s):  
Edgar Acuña ◽  
Jaime Porras
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Principal Components ◽  
Expression Data

Cancer data classification by quantum-inspired immune clone optimization-based optimal feature selection using gene expression data: deep learning approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nageswara Rao Eluri ◽  
Gangadhara Rao Kancharla ◽  
Suresh Dara ◽  
Venkatesulu Dondeti
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Selection Model ◽  
Expression Data ◽  
Content Type ◽  
Cancer Data ◽  
Optimal Feature Selection ◽  
Optimal Feature

PurposeGene selection is considered as the fundamental process in the bioinformatics field. The existing methodologies pertain to cancer classification are mostly clinical basis, and its diagnosis capability is limited. Nowadays, the significant problems of cancer diagnosis are solved by the utilization of gene expression data. The researchers have been introducing many possibilities to diagnose cancer appropriately and effectively. This paper aims to develop the cancer data classification using gene expression data.Design/methodology/approachThe proposed classification model involves three main phases: “(1) Feature extraction, (2) Optimal Feature Selection and (3) Classification”. Initially, five benchmark gene expression datasets are collected. From the collected gene expression data, the feature extraction is performed. To diminish the length of the feature vectors, optimal feature selection is performed, for which a new meta-heuristic algorithm termed as quantum-inspired immune clone optimization algorithm (QICO) is used. Once the relevant features are selected, the classification is performed by a deep learning model called recurrent neural network (RNN). Finally, the experimental analysis reveals that the proposed QICO-based feature selection model outperforms the other heuristic-based feature selection and optimized RNN outperforms the other machine learning methods.FindingsThe proposed QICO-RNN is acquiring the best outcomes at any learning percentage. On considering the learning percentage 85, the accuracy of the proposed QICO-RNN was 3.2% excellent than RNN, 4.3% excellent than RF, 3.8% excellent than NB and 2.1% excellent than KNN for Dataset 1. For Dataset 2, at learning percentage 35, the accuracy of the proposed QICO-RNN was 13.3% exclusive than RNN, 8.9% exclusive than RF and 14.8% exclusive than NB and KNN. Hence, the developed QICO algorithm is performing well in classifying the cancer data using gene expression data accurately.Originality/valueThis paper introduces a new optimal feature selection model using QICO and QICO-based RNN for effective classification of cancer data using gene expression data. This is the first work that utilizes an optimal feature selection model using QICO and QICO-RNN for effective classification of cancer data using gene expression data.

scholarly journals Classification of Gene Expression Data using Efficient Feature Selection Technique and Resampling Method

2019 ◽  
Vol 8 (6) ◽  
pp. 406-414
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Classification Model ◽  
Support Vector ◽  
Expression Data ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Resampling Method

Microarray technology has been developed as one of the powerful tools that have attracted many researchers to analyze gene expression level for a given organism. It has been observed that gene expression data have very large (in terms of thousands) of features and less number of samples (in terms of hundreds). This characteristic makes difficult to do an analysis of gene expression data. Hence efficient feature selection technique must be applied before we go for any kind of analysis. Feature selection plays a vital role in the classification of gene expression data. There are several feature selection techniques have been induced in this field. But Support Vector Machine with Recursive Feature Elimination (SVM-RFE) has been proven as the promising feature selection methods among others. SVM-RFE ranks the genes (features) by training the SVM classification model and with the combination of RFE method key genes are selected. Huge time consumption is the main issue of SVM-RFE. We introduced an efficient implementation of linier SVM to overcome this problem and improved the RFE with variable step size. Then, combined method was used for selecting informative genes. Effective resampling method is proposed to preprocess the datasets. This is used to make the distribution of samples balanced, which gives more reliable classification results. In this paper, we have also studied the applicability of common classifiers. Detailed experiments are conducted on four commonly used microarray gene expression datasets. The results show that the proposed method comparable classification performance

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