Dynamic rule-based similarity model for DNA microarray data

Abstract

Rules-based Similarity (RBS) is a framework in which concepts from rough set theory are used for learning a similarity relation from data. This paper presents an extension of RBS called Dynamic Rules-based Similarity model (DRBS) which is designed to boost the quality of the learned relation in case of highly dimensional data. Rules-based Similarity utilizes a notion of a reduct to construct new features which can be interpreted as important aspects of a similarity in the classification context. Having defined such features it is possible to utilize the idea of Tversky's feature contrast similarity model in order to design an accurate and psychologically plausible similarity relation for a given domain of objects. DRBS tries to incorporate a broader array of aspects of the similarity into the model by constructing many heterogeneous sets of features from multiple decision reducts. To ensure diversity, the reducts are computed on random subsets of objects and attributes. This approach is particularly well-suited for dealing with "few-objects-many- attributes" problem, such as mining of DNA microarray data. The induced similarity relation and the resulting similarity function can be used to perform an accurate classification of previously unseen objects in a case-based fashion. Experiments, whose results are also presented in the paper, show that the proposed model can successfully compete with other state-of-the-art algorithms such as Random Forest or SVM. © 2012 Springer-Verlag Berlin Heidelberg.