[Govindarajan, 3(12): December, 2014] ISSN: 2277-9655 Scientific Journal Impact Factor: 3.449 (ISRA), Impact Factor: 2.114 http: // www.ijesrt.com© International Journal of Engineering Sciences & Research Technology [387] IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY Development of Hybrid Ensemble Approach for Automobile Data M.Govindarajan * , A.Mishra * Assistant Professor, Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar – 608002, Tamil Nadu, India. Professor, Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India Abstract One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using an ensemble of classification methods for automobile data like Auto Imports and Car Evaluation Databases. In this research work, new hybrid classification method is proposed using classifiers in a heterogeneous environment using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using a Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. Here, modified training sets are formed by resampling from original training set; classifiers constructed using these training sets and then combined by voting. The proposed RBF-SVM hybrid system is superior to individual approach for Auto Imports and Car Evaluation Databases in terms of classification accuracy. Keywords: Machine learning, Radial Basis Function, Support Vector Machine, Ensemble, Classification Accuracy. Introduction Data mining methods may be distinguished by either supervised or unsupervised learning methods. In supervised methods, there is a particular pre-specified target variable, and they require a training data set, which is a set of past examples in which the values of the target variable are provided. Classification is a very common data mining task. In the process of handling classification tasks, an important issue usually encountered is determining the best performing method for a specific problem. Several studies address the issue. For example, Michie, Spiegelhalter, and Taylor [10] try to find the relationship between the best performing method and data types of input/output variables. Hybrid models have been suggested to overcome the defects of using a single supervised learning method, such as radial basis function and support vector machine techniques. Hybrid models combine different methods to improve classification accuracy. The goal of ensemble learning methods is to construct a collection (an ensemble) of individual classifiers that are diverse and yet accurate. If this can be achieved, then highly accurate classification decisions can be obtained by voting the decisions of the individual classifiers in the ensemble. The rest of this paper is organized as follows: Section 2 describes the related work. Section 3 presents hybrid intelligent system and Section 4 explains the performance evaluation measures. Section 5 focuses on the experimental results and discussion. Finally, results are summarized and concluded in section 6. Related work Data mining tasks like clustering, association rule mining, sequence pattern mining, and classification are used in many applications. Some of the widely used data mining algorithms in classification include Support vector machines and neural networks. Support vector machines (SVMs) are relatively new techniques that have rapidly gained popularity because of the excellent results N. Cristianini, et al. [2] have achieved in a wide variety of machine learning problems, and solid theoretical underpinnings in statistical learning theory.