Vilalta, Ricardo2015-02-112015-02-11May 20142014-05http://hdl.handle.net/10657/901We propose two pre-processing steps to classification that apply convex hull-based algorithms to the training set to help improve the performance and speed of classification. The Class Reconstruction algorithm uses a clustering algorithm combined with a convex hull-based approach that re-labels the dataset with a new and expanded class structure. We demonstrate how this performance-improvement algorithm helps boost the accuracy results of Naive Bayes in some, but not all, cases that use real-world datasets. The Class Size Reduction approach uses a clustering algorithm as well, followed by collecting all the clusters’ convex hulls to create a new, smaller dataset. This dataset allows for training a Support Vector Machine much faster. We also demonstrate the improvement in classification speed using this algorithm on several real-world datasets. The improvement in this case is more significant and consistent, with only a few cases where the accuracy dropped. The approaches for both projects are specially applicable to datasets that are characterized by a high number of clusters.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Convex hullData decompositionClass relabelingSVM optimizationNaïve BayesPerformance improvementComputer science2015-02-11Thesisborn digital