Performance Analysis of Automobile data using Bagged Ensemble Classifiers
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 12
Abstract
Data mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. The feasibility and the benefits of the proposed approaches are demonstrated by the means of Auto imports and Car Evaluation Databases. A variety of techniques have been employed for analysis ranging from traditional statistical methods to data mining approaches. Bagging and boosting are two relatively new but popular methods for producing ensembles. In this work, bagging is evaluated on Auto Imports and Car Evaluation Databases in conjunction with radial basis function and support vector machine as the base learners. The proposed bagged radial basis function and support vector machine is superior to individual approaches for Auto imports and Car Evaluation Databases in terms of classification accuracy.
Authors and Affiliations
M. Govindarajan, A. Mishra
Keywords
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