Comparison of Wrapper Based Feature Selection and Classifier Selection Methods for Drug Named Entity Recognition

Bioinformatics is a new yet quickly evolving interdisciplinary field that combines different other branches of science like biology and computer science. This field of science mainly relates to the process of extracting, categorizing and finally analyzing relevant biological data from large and not organized sources of information available. In this thesis, two machine-learning approaches, namely SVM and CRF have been performed for the recognition and classification of drugs and chemicals. These tasks are named as DrugNER and DrugNEC and have gained significant attention from the biomedical text mining community in recent years. Train and test datasets used in this work are derived from The DDI Corpus [1]. Three groups of features, morphological, lexical and orthographic are used. Wrapper based feature selection methods are used to find an optimal feature ensemble. In addition, wrapped based classifier selection algorithms are used in order to find an optimal set of classifiers from a large pool of CRF and SVM based classifiers. Results of both approaches have been compared. Finally a new majority voting algorithm, referred to as ranked-weighted majority voting is proposed and used during the combination of classifiers. Keywords: Biomedical Text Mining, Drug Name Entity Recognition, Feature Selection, Ranked-Weighted Majority Voting, Classifier Selection, Machine Learning, Support Vector Machines, Conditional Random Fields.