A sequential feature extraction approach for naïve bayes classification of microarray data

Fan, Liwei; Poh, Kim-Leng; Zhou, Peng

doi:10.1016/j.eswa.2009.01.075

Cited by 63 publications

(34 citation statements)

References 29 publications

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“…The major issue in Gene Classification is feature selection [2]. In the literature, statistical approaches like weighted voting scheme [3], nearest neighbor classification [4], discrimination methods [5] and least square and logistic regression [6] were used to develop the classifier model for gene expression data. These statistical approaches usually result in an inflexible classification system that is unable to classify a sample, if the expressions of genes are slightly different from the predefined profile.…”

Section: Introductionmentioning

confidence: 99%

Gene Expression Data Classification Using Support Vector Machine and Mutual Information-based Gene Selection

Vanitha¹,

Devaraj

Venkatesulu

2015

Procedia Computer Science

186

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DNA microarray technology can monitor the expression levels of thousands of genes simultaneously during important biological processes and across collections of related samples. Knowledge gained through microarray data analysis is increasingly important as they are useful for phenotype classification of diseases. This paper presents an effective method for gene classification using Support Vector Machine (SVM). SVM is a supervised learning algorithm capable of solving complex classification problems. Mutual information (MI) between the genes and the class label is used for identifying the informative genes. The selected genes are utilized for training the SVM classifier and the testing ability is evaluated using Leave-one-Out Cross Validation (LOOCV) method. The performance of the proposed approach is evaluated using two cancer microarray datasets. From the simulation study it is observed that the proposed approach reduces the dimension of the input features by identifying the most informative gene subset and improve classification accuracy when compared to other approaches.

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Section: Introductionmentioning

confidence: 99%

Gene Expression Data Classification Using Support Vector Machine and Mutual Information-based Gene Selection

Vanitha¹,

Devaraj

Venkatesulu

2015

Procedia Computer Science

186

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“…Despite its simplicity, Naive Bayes can often outperform more sophisticated classification methods. Naive Bayes classifier has gained popularity in solving various classification problems including microarray data analysis [14,15].…”

Section: B Naive Bayes Classifiermentioning

confidence: 99%

Classification of the green tea varieties based on Support Vector Machines using Terahertz spectroscopy

Chen

Zhang

Huang

et al. 2011

2011 IEEE International Instrumentation and Measurement Technology Conference

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Terahertz time-domain spectroscopy have been applied in research of four different varieties of Chinese green tea, the absorption and refractive Terahertz Spectrum of these tea were got in the range of 0.2 to 1.5 THz. Least Squares Support Vector Machines, Naive Bayes and Back Propagation Artificial Neural Network were applied to achieve Multi-class classification of these four kinds of tea, and the classification results of three algorithms were analyzed in detail. The results shows that support vector machine have better classification results in the experiment. This study demonstrated the feasibility of time-domain Terahertz Spectroscopy for the classification of difference kinds of tea.

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“…In the literature, statistical approaches like weighted voting scheme [3], nearest neighbor classification [4], discrimination methods [5], least square and logistic regression [6] and naive bayes approach [7] were used to generate the classifier model for gene expression data. These statistical approaches usually result in an inflexible classification system that is unable to classify a sample, if the expressions of genes differ slightly from the predefined profile.…”

mentioning

confidence: 99%

Hybrid Ant Bee Algorithm for Fuzzy Expert System Based Sample Classification

Ganeshkumar

Rani²,

Devaraj

et al. 2014

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Accuracy maximization and complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant colony optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs artificial bee colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches.

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A sequential feature extraction approach for naïve bayes classification of microarray data

Cited by 63 publications

References 29 publications

Gene Expression Data Classification Using Support Vector Machine and Mutual Information-based Gene Selection

Gene Expression Data Classification Using Support Vector Machine and Mutual Information-based Gene Selection

Classification of the green tea varieties based on Support Vector Machines using Terahertz spectroscopy

Hybrid Ant Bee Algorithm for Fuzzy Expert System Based Sample Classification

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