Title: Dynamic classifier chains for multi-label learning,
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.