User-Oriented Feature Selection for Machine Learning

The effectiveness of any machine learning algorithm depends, to a large extent, on the selection of a good subset of features or attributes. Most existing methods use the syntactic or statistical information of the data, relying on a heuristic criterion to select features. In this paper, we investigate an alternative less-studied approach called user-oriented feature selection by exploiting the domain-specific semantic information. Given any two features, a user is able to express which one is more important based on the semantic consideration. Such user requirements are formally described by a preference relation on the set of features. Algorithms are proposed to construct a subset of features that is most consistent with the user requirements. Their properties and computational complexity are analysed. User-oriented feature selection offers a new view for machine learning and its potentials need to be further investigated and explored.