A hybrid approach to increase the performance of protein folding recognition using support vector machines

Authors:
Lavneet Singh;Girija Chetty;Dharmendra Sharma
Affiliations:
Faculty of Information Sciences & Engineering, University of Canberra, Australia;Faculty of Information Sciences & Engineering, University of Canberra, Australia;Faculty of Information Sciences & Engineering, University of Canberra, Australia
Venue:
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2012

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Abstract

In area of bioinformatics, large amount of data is being harvested with functional and genetic features of proteins. The data is being generated consists of thousands of features with least observations instances. In such case, we need computational tools to analyze and extract useful information from vast amount of raw data which help in predicting the major biological functions of genes and proteins with respect to their structural behavior. Thus, in this study, we use a new hybrid approach for features selection and classifying data using Support Vector Machine (SVM) classifiers with Quadratic Discriminant Analysis (QDA) as generative classifiers to increase more performance and accuracy. We compare our results with previous results and seem to be much promising. The proposed method provides the higher recognition ratio rather than other method used in previous studies. The obtained results are also compared with other different classifiers and our hybrid classifiers give more accuracy and achieve better results than any other classifiers.