On the role of feature space granulation in feature selection processes

Information granulation plays an important role in the process of scaling up modern machine learning and knowledge discovery algorithms. By employing compact descriptions of granules — whereby granules are defined as collections of original data elements gathered together by means of their similarity, proximity or functionality — one can drastically accelerate computations and, moreover, make the results of those computations more meaningful for domain experts. In this paper, we summarize some of the feature space granulation approaches introduced by now. We discuss the meaning of similarity, proximity and functionality while considering the granules of physically existing or potentially derivable attributes. We also show several examples of utilization of the granulation structures defined over the feature spaces in the feature selection algorithms. As a case study, we consider the algorithms developed within the theory of rough sets, aimed at finding irreducible subsets of attributes that are sufficient to distinguish between the cases belonging to different target decision classes.