Ensembles of Nested Dichotomies with Multiple Subset Evaluation
This work addresses performance issues in multi-class classification methods, but it is incremental as it builds on existing nested dichotomy techniques.
The paper tackles the problem of improving predictive performance in nested dichotomies for multi-class classification by introducing a method that enhances any random subset selection technique, resulting in reduced root mean squared error as shown empirically.
A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Many methods have been proposed to perform this split, each with various advantages and disadvantages. In this paper, we present a simple, general method for improving the predictive performance of nested dichotomies produced by any subset selection techniques that employ randomness to construct the subsets. We provide a theoretical expectation for performance improvements, as well as empirical results showing that our method improves the root mean squared error of nested dichotomies, regardless of whether they are employed as an individual model or in an ensemble setting.