The Max-Cut Decision Tree: Improving on the Accuracy and Running Time of Decision Trees

Abstract

Decision trees are a widely used method for classification, both by themselves and as the building blocks of multiple different ensemble learning methods. The Max-Cut decision tree involves novel modifications to a standard, baseline model of classification decision tree construction, precisely CART Gini. One modification involves an alternative splitting metric, maximum cut, based on maximizing the distance between all pairs of observations belonging to separate classes and separate sides of the threshold value. The other modification is to select the decision feature from a linear combination of the input features constructed using Principal Component Analysis (PCA) locally at each node. Our experiments show that this node-based localized PCA with the novel splitting modification can dramatically improve classification, while also significantly decreasing computational time compared to the baseline decision tree. Moreover, our results are most significant when evaluated on data sets with higher dimensions, or more classes; which, for the example data set CIFAR-100, enable a 49% improvement in accuracy while reducing CPU time by 94%. These introduced modifications dramatically advance the capabilities of decision trees for difficult classification tasks.

Comparative Study of Chronic Kidney Disease Predictor Performance Given Insufficient Training Dataset

“…Each branch represents the outcome of the test made and each leaf node obtained represents the final decision taken. A variant of the tree algorithm known as classification and regression tree works by using the Gini index to split the training set into subsets using a feature-threshold value pair, selecting the subset with more decrease in impurity [7], [8]. The two models described above have been employed over the years in the classification of a variety of datasets.…”