Title :
Support Vector Machine ensembles using features distribution among subsets for enhancing microarray data classification
Author :
Ahmed, Eman ; El-Gayar, Neamat ; El-Azab, Iman A.
Author_Institution :
Fac. of Comput. & Inf., Cairo Univ., Cairo, Egypt
fDate :
Nov. 29 2010-Dec. 1 2010
Abstract :
Support Vector Machines (SVMs) ensembles have been widely used to improve classification accuracy in complicated pattern recognition tasks. In this work we propose to apply an ensemble of SVMs coupled with feature-subset selection methods to aleviate the curse of dimensionality associated with expression-based classification of DNA microarray data. We compare the single SVM classifier to SVM ensembles applying two different feature-subset selection techniques, namely random selection and k-means clustering, the base classifiers are combined using either majority vote or SVM fusion. Two real-world benchmarks datasets are used to evaluate and compare the performance. Experimental results show that the SVM ensemble of SVM base classifiers using k-means clustering for feature-subset selection and employing an SVM combiner achieve the best classification accuracy, and that feature-subset-selection methods can have considerable impact on the classification accuracy.
Keywords :
DNA; biology computing; data mining; lab-on-a-chip; pattern classification; pattern clustering; support vector machines; DNA microarray; SVM fusion; feature distribution; feature subset selection method; k-means clustering; majority vote; microarray data classification; pattern recognition; random selection; support vector machine ensembles; Ensemble classification; Feature selection; Feature subsets; Microarray data; SVM fusion; Support Vector Machines (SVM);
Conference_Titel :
Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on
Conference_Location :
Cairo
Print_ISBN :
978-1-4244-8134-7
DOI :
10.1109/ISDA.2010.5687078