Comparative Analysis of Algorithms in Supervised Classification: A Case study of Bank Notes Dataset

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), Na?ve 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 Na?ve 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 Na?ve Bayes in terms of accuracy from both standpoints of holdout and cross validation.