Using ANOVA to Analyze Modified Gini Index Decision Tree Classification.
Quôc-Nam Trân · DMIN · 2008
Decision tree classification is a commonly used method in data mining. It has been used for predicting medical diagnoses. Among data mining methods for classification, decision trees have several advantages such as they are simple to understand and interpret; they are able to handle both numerical and categorical attributes. However, it is well-known that when Gini index is used for classification, the method biases multivalued attributes. In addition to having difficulty when the number of classes is large, the method also tends to favor tests that result in equal-sized partitions and purity in all partitions. We modified the Gini-based decision tree method. To overcome the known problems, we normalize the Gini indexes by taking into account information about the splitting status of all attributes. Instead of using the Gini index for attribute selection as usual, we use ratios of Gini indexes and their splitting values in order to reduce the biases. In this paper, we utilize ANOVA, an analysis of variance method, to show that our modified Gini decision tree method is statistically different with other known decision tree methods at least for the tested benchmark medical data bases.