期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2014
卷号：17
期号：1
页码：39-43
DOI：10.14445/22312803/IJCTT-V17P109
出版社：Seventh Sense Research Group
摘要：There are different techniques in conducting data mining that range from clustering, association rule mining, prediction and classification. These techniques are applied using learning algorithms such as Support Vector Machines (SVM), Naïve Bayes, and Artificial Neural Network (ANN). When conducting data mining, the choice of algorithm to use is an important decision because it depends on factors such as the nature or type of data under examination, and the target outcome of the data mining activity. In this study, we compare Naïve Bayes and Multilayer Perceptron using the classification technique as a case study on the Bank Notes dataset from the University of California Irvine (UCI) from two standpoints, which are; holdout and cross validation. Result from experiments show Multilayer Perceptron outperforms Naïve Bayes in terms of accuracy from both standpoints of holdout and cross validation.
