Computational intelligence approaches for classification and temporal clustering in bioinformatics

Unprecedented amount of data coming from various high-throughput techniques in biomedical research has presented challenges to computational intelligence (CI) methods to develop efficient algorithms for extraction of knowledge from them. These challenges can be put into one or more of the following different CI perspectives: Feature Selection or feature extraction. Prediction (Classification/Regression). Discovery of new classes (Clustering). This thesis has studied problems in computational biology domain with above perspectives and developed efficient CI methods for them. The research work can be categorized into three parts. First part discusses multi-class classification problems. A framework of class-wise feature selection with One-versus-all-support vector machine (OVA-SVM) classifier is studied. In this framework three methods of probability conversion methods of decision function values of SVM and their ensemble are studied and compared. The proposed framework for multiclass classification has two distinguishing characteristics. First, it uses class-wise optimized features and second, decisions of different SVMs classifiers are coupled with probability estimates to make final prediction. The proposed approach is used for multi-class classification of several biological datasets. It obtained better or at least competitive predictive accuracy to the current best methods for the selected datasets. In all three cases, brief analysis is performed on class-wise selected features to verify their biological relevance to the associated class.Doctor of Philosoph