Condensing multi-label data based on Clustering

A common approach to speed up the instance-based classifiers, while preserving accuracy, is to use a subset of the original training set.This process, known as condensing, reduces the training dataset to a smaller, representative set.This is accomplished by employing a data reduction technique that selects the most relevant training instances.While many of these techniques have been successfully applied to single-label classification, most are not appropriate for multi-label data, where instances can be associated with multiple classes.This paper proposes a simple data reduction technique for multi-label datasets utilizing -means++ clustering.The proposed method selects instances near cluster centroids to form the condensing set, utilizing -means++'s initialization strategy to achieve widely spread initial centroids.Experiments conducted on nine datasets, combined with a statistical test, show that our approach achieves significant data reduction while maintaining high classification accuracy.