Empirical Investigation of CART and Decision Tree Extraction from Neural Networks
Accuracy is a critical factor in predictive modeling. A predictive model such as a decision tree must be accurate to draw conclusions about a prediction. This research aims at analyzing and improving the performance of classification and regression trees(CART), a decision tree algorithm by evaluating the performance of the algorithm on a
set of databases extracted from real world problems. Various methods and parameters of the algorithm were used to develop decision trees. The predictive accuracy of the trees developed by all the methods was examined and a best method that develops a tree with better accuracy was identified. However, a new approach was introduced to further
improve the efficiency of the CART algorithm by combining the functionality of CART with neural networks. Neural networks contribute by generating new data necessary to improve the accuracy of the decision trees. Finally, the decision trees developed by both the new method and existing CART were compared for accuracy. This research thus provides insight into improved performance of the CART algorithm, comprehending the behavior of the algorithm and determining methods and parameters for better accuracy.
School Location:USA - Ohio
Source Type:Master's Thesis
Keywords:classification and regression trees trepan enhanced cart decision tree algorithm
Date of Publication:04/27/2009