Robust Linear Discriminant Trees
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:285-291, 1995.
We present a new method for the induction of classification trees with linear discriminants as the partitioning function at each internal node. This paper presents two main contributions: first, a novel objective function called soft entropy which is used to identify optimal coefficients for the linear discriminants, and second, a novel method for removing outliers called iter ative re-filtering which boosts performance on many datasets. These two ideas are presented in the context of a single learning algorithm called DT-SEPIR.