Dynamic classifier chains for multi-label learning

TL;DR

This paper introduces a dynamic ensemble of chain classifiers for multi-label classification that adapt label order per instance, improving efficiency and reducing error propagation using heuristics and simple classifiers.

Contribution

It proposes two novel algorithms for dynamic label ordering in classifier chains, enabling instance-specific configurations without rebuilding models.

Findings

Naive Bayes-based dynamic chains outperform static models.

Heuristic effectively reduces error propagation.

Method is computationally efficient.

Abstract

In this paper, we deal with the task of building a dynamic ensemble of chain classifiers for multi-label classification. To do so, we proposed two concepts of classifier chains algorithms that are able to change label order of the chain without rebuilding the entire model. Such modes allows anticipating the instance-specific chain order without a significant increase in computational burden. The proposed chain models are built using the Naive Bayes classifier and nearest neighbour approach as a base single-label classifiers. To take the benefits of the proposed algorithms, we developed a simple heuristic that allows the system to find relatively good label order. The heuristic sort labels according to the label-specific classification quality gained during the validation phase. The heuristic tries to minimise the phenomenon of error propagation in the chain. The experimental results showed that the proposed model based on Naive Bayes classifier the above-mentioned heuristic is an efficient tool for building dynamic chain classifiers.