Tree-Based Dynamic Classifier Chains

TL;DR

This paper introduces a dynamic classifier chain method using decision trees that adaptively choose label orderings for each instance, improving accuracy and efficiency in multi-label classification.

Contribution

It proposes a novel dynamic label ordering approach with decision trees, outperforming static methods and speeding up training and prediction.

Findings

Dynamic label selection improves prediction accuracy.

Tree-based models outperform static classifiers.

Method accelerates training and inference processes.

Abstract

Classifier chains are an effective technique for modeling label dependencies in multi-label classification. However, the method requires a fixed, static order of the labels. While in theory, any order is sufficient, in practice, this order has a substantial impact on the quality of the final prediction. Dynamic classifier chains denote the idea that for each instance to classify, the order in which the labels are predicted is dynamically chosen. The complexity of a naive implementation of such an approach is prohibitive, because it would require to train a sequence of classifiers for every possible permutation of the labels. To tackle this problem efficiently, we propose a new approach based on random decision trees which can dynamically select the label ordering for each prediction. We show empirically that a dynamic selection of the next label improves over the use of a static ordering under an otherwise unchanged random decision tree model. % and experimental environment. In addition, we also demonstrate an alternative approach based on extreme gradient boosted trees, which allows for a more target-oriented training of dynamic classifier chains. Our results show that this variant outperforms random decision trees and other tree-based multi-label classification methods. More importantly, the dynamic selection strategy allows to considerably speed up training and prediction.