NDLI: Ensemble-based classifiers for cancer classification using human tumor microarray data

Content Provider	IEEE Xplore Digital Library
Author	Margoosian, A. Abouei, J.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Yazd Univ., Yazd, Iran (Margoosian, A.; Abouei, J.)
Abstract	In this paper, two cancer classification techniques based on multicategory microarray data sets are presented. Due to the high dimensionality of microarray data sets, choosing reliable feature selection and classification algorithms with a high degree of accuracy and a low complexity is a crucial task in bioinformatics. Toward this goal, this paper aims to maximize the cancer classification accuracy using two reliable ensemble-based classifiers namely the ensemble of naive bayes and the ensemble of k-nearest neighbor. Simulation results show that our classifiers have considerably better accuracy than some conventional classification techniques such as the Support Vector Machine (SVM) and artificial neural networks in the field of multicategory microarray cancer classification based on fourteen cancer data set. However, the run time of the introduced ensemble-based classifiers is longer when the schemes use whole features. To reduce the time complexity while preserving the same classification accuracy as before, we use the recursive feature elimination based on the multiple support vector machine classifier to select more informative genes before applying the ensemble-based classifiers. Numerical evaluations show at least 30% improvement in the classification accuracy of our schemes when compared to the SVM-one versus one rule. In addition, our schemes are much more robust to the feature elimination and display a high accuracy in the case of low number of features.
Starting Page	1
Ending Page	6
File Size	271736
Page Count	6
File Format	PDF
ISBN	9781467356343
DOI	10.1109/IranianCEE.2013.6599553
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-14
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Naive bayes classifier Accuracy Simulation Microarray data set Recursive feature elimination Computational complexity Cancer classification Ensemble-based methods Cancer
Content Type	Text
Resource Type	Article

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