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dc.contributor.advisorAgarwal, Shivani
dc.contributor.advisorVeni Madhavan, C E
dc.contributor.authorSaneem Ahmed, C G
dc.date.accessioned2018-02-17T22:26:00Z
dc.date.accessioned2018-07-31T04:38:54Z
dc.date.available2018-02-17T22:26:00Z
dc.date.available2018-07-31T04:38:54Z
dc.date.issued2018-02-18
dc.date.submitted2014
dc.identifier.urihttp://etd.iisc.ac.in/handle/2005/3138
dc.identifier.abstracthttp://etd.iisc.ac.in/static/etd/abstracts/3992/G27114-Abs.pdfen_US
dc.description.abstractThe problem of feature selection is critical in several areas of machine learning and data analysis such as, for example, cancer classification using gene expression data, text categorization, etc. In this work, we consider feature selection for supervised learning problems, where one wishes to select a small set of features that facilitate learning a good prediction model in the reduced feature space. Our interest is primarily in filter methods that select features independently of the learning algorithm to be used and are generally faster to implement compared to other types of feature selection algorithms. Many common filter methods for feature selection make use of information-theoretic criteria such as those based on mutual information to guide their search process. However, even in simple binary classification problems, mutual information based methods do not always select the best set of features in terms of the Bayes error. In this thesis, we develop a general approach for selecting a set of features that directly aims to minimize the Bayes error in the reduced feature space with respect to the loss or performance measure of interest. We show that the mutual information based criterion is a special case of our setting when the loss function of interest is the logarithmic loss for class probability estimation. We give a greedy forward algorithm for approximately optimizing this criterion and demonstrate its application to several supervised learning problems including binary classification (with 0-1 error, cost-sensitive error, and F-measure), binary class probability estimation (with logarithmic loss), bipartite ranking (with pairwise disagreement loss), and multiclass classification (with multiclass 0-1 error). Our experiments suggest that the proposed approach is competitive with several state-of-the art methods.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesG27114en_US
dc.subjectData Analysisen_US
dc.subjectLogarithmsen_US
dc.subjectSupervised Learningen_US
dc.subjectBayes Optimalityen_US
dc.subjectBinary Classsificationen_US
dc.subjectBipartite Rankingen_US
dc.subjectMulticlass Classificationen_US
dc.subjectBayes Optimal Feature Selectionen_US
dc.subjectOptimal Feature Selectionen_US
dc.subjectBayes Erroren_US
dc.subjectBinary Class Probability Estimationen_US
dc.subjectSupervised Learning Problemsen_US
dc.subject.classificationComputer Scienceen_US
dc.titleBayes Optimal Feature Selection for Supervised Learningen_US
dc.typeThesisen_US
dc.degree.nameMSc Enggen_US
dc.degree.levelMastersen_US
dc.degree.disciplineFaculty of Engineeringen_US


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