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Decision trees are a widely used method for classification, both bythemselves and as the building blocks of multiple different ensemble learningmethods. The Max-Cut decision tree involves novel modifications to a standard,baseline model of classification decision tree construction, precisely CARTGini. One modification involves an alternative splitting metric, maximum cut,based on maximizing the distance between all pairs of observations belonging toseparate classes and separate sides of the threshold value. The othermodification is to select the decision feature from a linear combination of theinput features constructed using Principal Component Analysis (PCA) locally ateach node. Our experiments show that this node-based localized PCA with thenovel splitting modification can dramatically improve classification, whilealso significantly decreasing computational time compared to the baselinedecision tree. Moreover, our results are most significant when evaluated ondata sets with higher dimensions, or more classes; which, for the example dataset CIFAR-100, enable a 49% improvement in accuracy while reducing CPU time by94%. These introduced modifications dramatically advance the capabilities ofdecision trees for difficult classification tasks.

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