Performance analysis of cart and C5.0 using sampling techniques
Merlin Balamurugan -, Sathya Kannan · 2016
Data mining is the process of extracting the hidden predictive model from large databases. It has various methods and algorithms. Classification is a supervised method, which builds a model for predicting the new instances. Different algorithms like decision tree, neural networks, support vector machines, k nearest neighbour, Bayesian classification are available for the classification. Decision tree is the simple and most commonly used algorithm among the classification algorithms. It constructs a tree based model on the values of feature and generates rules for decision making. Samples are used for classification in order to train the model and predict the new instances. Unbiased samples can improve the performance of classification. This paper analyses the performance of CART and C5.0 algorithms using sampling techniques.