Dynamic classifier chains for multi-label learning
This work addresses multi-label classification by improving efficiency and reducing error propagation, though it is incremental as it builds on existing classifier chain methods.
The paper tackled the problem of building dynamic classifier chains for multi-label learning by proposing algorithms that adjust label order without rebuilding the model, using Naive Bayes and nearest neighbor classifiers with a heuristic to minimize error propagation, resulting in an efficient tool as shown in experiments.
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.