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Performance evaluation of MLPNN and NB: A Comparative Study on Car Evaluation Dataset


Zia Ul Rehman, Hira Fayyaz, Asghar Ali Shah, Numan Aslam, Muhammad Hanif, Sagheer Abbas


Vol. 18  No. 9  pp. 144-147


Cars are vital in everyday life. It plays an important role as it’s a comfortable mean of transportations. Every car has a distinct flavor in term of price, feature, safety and the level of luxury it provides. People tend to make clear choices when they decide to buy car for themselves. They evaluate different cars on various parameters. Manufacturing and business are interested to know the popular features on which buyers make their choice as it can enhance their business value. Data mining algorithms can be employed in this respect. Various data mining algorithms perform differently. The purpose of this research work is to equating two influential algorithm evaluating dataset acquired from the University of California Irvine. This research focuses on comparing and contrasting speed, accuracy and performance of these algorithms.


Multi-layer perceptron (MLPNN), Na?ve Bayesian (NB), Artificial Neural Network (ANN)