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文章基本信息

  • 标题:Performance evaluation of MLPNN and NB: A Comparative Study on Car Evaluation Dataset
  • 作者:Zia Ul Rehman ; Hira Fayyaz ; Asghar Ali Shah
  • 期刊名称:International Journal of Computer Science and Network Security
  • 印刷版ISSN:1738-7906
  • 出版年度:2018
  • 卷号:18
  • 期号:9
  • 页码:144-147
  • 出版社:International Journal of Computer Science and Network Security
  • 摘要: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.
  • 关键词:Multi-layer perceptron (MLPNN); Na?ve Bayesian (NB); Artificial Neural Network (ANN)
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