Ensembles of Nested Dichotomies with Multiple Subset Evaluation

TL;DR

This paper introduces a simple, general method to enhance the predictive accuracy of nested dichotomies in multi-class classification by leveraging subset evaluation techniques with randomness, supported by theoretical and empirical evidence.

Contribution

It proposes a novel, broadly applicable approach to improve nested dichotomies using random subset evaluation, with theoretical analysis and empirical validation.

Findings

Improves root mean squared error of nested dichotomies

Enhances performance in both individual and ensemble models

Theoretical expectation supports observed improvements

Abstract

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.