Microarray Gene Expression Data Classification Using Feature Selection and Naïve Bayes Classifier

2015 ◽  
Vol 3 (3) ◽  
Author(s):  
Halit Vural ◽  
Abdülhamit Subaşı
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Naive Bayes ◽  
Data Classification ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Bayes Classifier ◽  
Microarray Gene Expression ◽  
Microarray Gene

scholarly journals Robustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Md. Shakil Ahmed ◽  
Md. Shahjaman ◽  
Md. Masud Rana ◽  
Md. Nurul Haque Mollah
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Naive Bayes ◽  
Microarray Gene Expression Data ◽  
Naïve Bayes ◽  
Expression Data ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Microarray Gene Expression

The naïve Bayes classifier (NBC) is one of the most popular classifiers for class prediction or pattern recognition from microarray gene expression data (MGED). However, it is very much sensitive to outliers with the classical estimates of the location and scale parameters. It is one of the most important drawbacks for gene expression data analysis by the classical NBC. The gene expression dataset is often contaminated by outliers due to several steps involved in the data generating process from hybridization of DNA samples to image analysis. Therefore, in this paper, an attempt is made to robustify the Gaussian NBC by the minimum β-divergence method. The role of minimum β-divergence method in this article is to produce the robust estimators for the location and scale parameters based on the training dataset and outlier detection and modification in test dataset. The performance of the proposed method depends on the tuning parameter β. It reduces to the traditional naïve Bayes classifier when β→0. We investigated the performance of the proposed beta naïve Bayes classifier (β-NBC) in a comparison with some popular existing classifiers (NBC, KNN, SVM, and AdaBoost) using both simulated and real gene expression datasets. We observed that the proposed method improved the performance over the others in presence of outliers. Otherwise, it keeps almost equal performance.

Hybrid Feature Selection Algorithm mRMR-ICA for Cancer Classification from Microarray Gene Expression Data

2018 ◽  
Vol 21 (6) ◽  
pp. 420-430 ◽  
Author(s):  
Shuaiqun Wang ◽  
Wei Kong ◽  
Aorigele ◽  
Jin Deng ◽  
Shangce Gao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Classification Accuracy ◽  
Microarray Gene Expression Data ◽  
Cancer Classification ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Redundant Genes ◽  
Microarray Gene

Aims and Objective: Redundant information of microarray gene expression data makes it difficult for cancer classification. Hence, it is very important for researchers to find appropriate ways to select informative genes for better identification of cancer. This study was undertaken to present a hybrid feature selection method mRMR-ICA which combines minimum redundancy maximum relevance (mRMR) with imperialist competition algorithm (ICA) for cancer classification in this paper. Materials and Methods: The presented algorithm mRMR-ICA utilizes mRMR to delete redundant genes as preprocessing and provide the small datasets for ICA for feature selection. It will use support vector machine (SVM) to evaluate the classification accuracy for feature genes. The fitness function includes classification accuracy and the number of selected genes. Results: Ten benchmark microarray gene expression datasets are used to test the performance of mRMR-ICA. Experimental results including the accuracy of cancer classification and the number of informative genes are improved for mRMR-ICA compared with the original ICA and other evolutionary algorithms. Conclusion: The comparison results demonstrate that mRMR-ICA can effectively delete redundant genes to ensure that the algorithm selects fewer informative genes to get better classification results. It also can shorten calculation time and improve efficiency.

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