Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains

TL;DR

This paper introduces novel Monte Carlo algorithms to improve classifier chains for multi-label classification, enhancing performance and computational efficiency in high-dimensional data.

Contribution

The paper proposes new Monte Carlo methods for optimizing chain sequences and inference in classifier chains, addressing computational challenges in high-dimensional multi-label classification.

Findings

Achieves state-of-the-art predictive performance on multiple datasets.

Maintains computational tractability for high-dimensional data.

Improves chain sequence selection and inference efficiency.

Abstract

Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance - at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest- performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.