Ensembles of classifiers and their aplication to bioinformatics problems

Summary form only given. Machine Learning has become a very popular approach in addressing problems in the Computational Biology and Bioinformatics area. In addition, multi-classifier systems have also gained popularity among researchers working in machine learning and applications for their ability to fuse together multiple models and obtain better overall accuracy and classification results. This talk is concerned with current issues in the design of multi-classifier systems and presents some multi-classifier developments for several bioinformatics problems. The talk will first present an overview and current status of machine learning methods in bioinformatics and computational biology. The talk will then bring in some important issues in building ensembles of classifiers, with a focus on the diversity and combination of individual classifiers. Few diversification and combination schemes are presented along with guidelines for the selection of different training paradigms and performance metrics, based on the properties and distribution of the data. Then, the presentation will proceed with introducing our computational intelligence based multi-classifier developments for solving several bioinformatics problems, such as recognizing sequences in DNA strings, micro-array gene expression data analysis, protein structure prediction. The talk will also present related machine learning issues in developing such systems, such as learning from imbalanced datasets and using appropriate performance metrics for model selection and evaluation. The presented approaches and results will advocate that ensembles of classifiers can be used as effective modelling tools in solving challenging bioinformatics problems.