Multivariate multi-layer classifier

Abstract

The variance-ratio binary multi-layer classifier (VRBMLC) has been recently proposed and shown to outperform conventional binary decision trees (BDTs). Though effective with better interpretability, the VRBMLC generates deep layers of tree nodes as it employs a one-feature-at-a-time binary split at each layer. To further condense the tree depth and enhance the classification performance, this research proposes a multivariate multi-layer classifier that applies a variance-ratio criterion to enable ternary splits of each tree node and that integrates the oblique discriminant hyperplane in the tree node. We benchmark 16 state-of-the-art univariate and multivariate classifiers on 43 publicly available datasets. The results show that the proposed methods greatly simplify the tree structure and yield a significantly higher average accuracy.