Multimodal Classification: Case Studies

Data models that are induced in classifier construction often consist of multiple parts, each of which explains part of the data. Classification methods for such multi-part models are called multimodal classification methods. The model parts may overlap or have insufficient coverage. How to deal best with the problems of overlapping and insufficient coverage? In this paper we propose a hierarchical or layered approach to this problem. Rather than seeking a single model, we consider a series of models under gradually relaxing conditions, which form a hierarchical structure. To demonstrate the effectiveness of this approach we consider two classifiers that construct multi-part models – one based on the so-called lattice machine and the other one based on rough set rule induction, and we design hierarchical versions of the two classifiers. The two hierarchical classifiers are compared through experiments with their non-hierarchical counterparts, and also with a method that combines k-nearest neighbors classifier with rough set rule induction as a benchmark. The results of the experiments show that this hierarchical approach leads to improved multimodal classifiers.