scholarly journals Optimal feature selection for sparse linear discriminant analysis and its applications in gene expression data

2013 ◽  
Vol 66 ◽  
pp. 140-149 ◽  
Author(s):  
Cheng Wang ◽  
Longbing Cao ◽  
Baiqi Miao
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Gene Expression Data ◽  
Expression Data ◽  
Linear Discriminant ◽  
Optimal Feature Selection ◽  
Selection For ◽  
Optimal Feature

Cancer Data Classification by Quantum Inspired Immune Clone Optimization-based Optimal Feature Selection using Gene Expression Data: Deep Learning Approach (Preprint)

2020 ◽  
Author(s):  
Nageswara Rao Eluri
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Cancer Diagnosis ◽  
Classification Accuracy ◽  
Expression Data ◽  
Cancer Data ◽  
Optimal Feature Selection ◽  
Optimal Feature

UNSTRUCTURED Gene selection is considered as the fundamental process under the bioinformatics field, as the cancer classification accuracy completely focused on the genes, which provides biological relevance to the classifying problems. The accurate classification of diverse types of tumor is seeking immense demand in the cancer diagnosis task. However, the existing methodologies pertain to cancer classification are mostly clinical basis, and so its diagnosis capability is limited. Nowadays, the significant problems of cancer diagnosis are solved by the utilization of gene expression data, by which, the researchers have been introducing many possibilities to diagnose cancer in an appropriate and effective way. This paper plans to develop the cancer data classification using gene expression data. Initially, five benchmark gene expression datasets, i.e., “Colon cancer, defused B-cell Lymphoma, Leukaemia, Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer Data” are collected for performing the experiment. The proposed classification model involves three main phases: “(a) Feature extraction, (b) Optimal Feature Selection, and (c) Classification”. From the collected gene expression data, the feature extraction is performed using the first order and second-order statistical measures after data pre-processing. In order to diminish the length of the feature vectors, optimal feature selection is performed, in 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). Moreover, the number of hidden neurons of RNN is optimized by the same Q-ICOA. The optimal feature selection and classification is performed for selecting the most suitable features and thus maximizing the classification accuracy. Finally, the experimental analysis reveals that the proposed model outperforms the QICO-based feature selection over other heuristic-based feature selection and optimized RNN over other machine learning algorithms

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.

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