Performance Investigation of different Classification Algorithms Using Waikato Environment for Knowledge Analysis

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G.Victor Daniel
G.Sunny Demoli,P.Prathyusha, K.Suresh Kumar


Data mining refers to the task of extracting knowledge or hidden interesting patterns from the large volumes of data. Classification is one of the data mining functionalities which refer to the process of finding a model to predict the class label of objects whose class is unknown. This paper analyze the four major classification algorithms such as Naïve Bayes classifier, ADTree classifier, PART Rule based classifier and Kstar classifier using Waikato Environment for Knowledge Analysis or in short, WEKA. The aim of this paper is to investigate the performance of the classification algorithms on the aspect of correctly classified instances. The data ‘vote data’ with a total data of 7395 and a dimension of 435 rows and 17 columns are used to experiment and to rationalize different classification algorithms. The performance of these four algorithms are presented and compared.


Keywords: Clsssification Algorithms; Weka; Performance Evaluation Of Machine Learning Algorithms


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