Paper Details: Downloads: 466
Serial Number: P1121101447
Title: Employing Neural Network and Naive Bayesian Classifier in Mining Data for Car Evaluation
Authors: S. Makki and J. M. Kassim and E. H. Gharayebeh and M. Alhazmi and A. Mustapha
Abstract: In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian Classifier (NBC). In this paper, an investigation has been performed to compare these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that BBNN yield higher accuracy as compared to NBC but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.
Keywords: Data mining, Backpropagation Neural Network, Naïve Bayesian Classifier, Classification
Journal/Conference: ICGST Conference on Artificial Intelligence and Machine Learning, AIML-11
Volume:
Issue:
Submission Date: 1/3/2011 12:00:00 AM
Review Date: 3/27/2011 12:00:00 AM
Publishing Date: 4/6/2011 12:00:00 AM
Article Downloads: 466
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