Data reduction via multi-label prototype generation

Abstract

A very common practice to speed up instance based classifiers is to reduce the size of their training set, that is, replace it by a condensing set, hoping that their accuracy will not worsen. This can be achieved by applying a Prototype Selection or Generation algorithm, also referred to as a Data Reduction Technique. Most of these techniques cannot be applied on multi-label problems, where an instance may belong to more than one classes. Reduction through Homogeneous Clustering (RHC) and Reduction by Space Partitioning (RSP3) are parameter-free single-label Prototype Generation algorithms. Both are based on recursive data partitioning procedures that identify homogeneous clusters of training data, which they replace by their representatives. This paper proposes variations of these algorithms for multi-label training datasets. The proposed methods generate multi-label prototypes and inherit all the desirable properties of their single-label versions. They consider clusters that contain instances that share at least one common label as homogeneous clusters. It is shown via an experimental study based on nine multi-label datasets that the proposed algorithms achieve good reduction rates without negatively affecting classification accuracy.