Rough Set Extensions for Feature Selection
Neil Mac Parthaláin · 2009
Rough set theory (RST) was proposed as a mathematical tool to deal with the analysis of imprecise, uncertain or incomplete information or knowledge. It is of fundamental importance to artificial intelligence particularly in the areas of knowledge discovery, machine learning, decision support systems, and inductive reasoning. At the heart of RST is the idea of only employing the information contained within the data, thus unlike many other methods, probability distribution information or assignments are not required. RST relies on the concept of indiscernibility to group equivalent elements and generate knowledge granules. These granules are then used to build a structure to approximate a given concept. This framework has unsurprisingly proven successful for the application to the task of feature selection. Feature selection (FS) is a term given to the problem of selecting input attributes which are most predictive of a given outcome. Unlike other dimensionality reduction methods, feature selection algorithms preserve the original semantics of the features following reduction. This has been applied to tasks which involve