Discernibility matrix simplification for constructing attribute reducts
Information Sciences: an International Journal
Fuzzy-rough attribute reduction via mutual information with an application to cancer classification
Computers & Mathematics with Applications
Fuzzy rough set and information entropy based feature selection for credit scoring
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
A model of user-oriented reduct construction for machine learning
Transactions on rough sets VIII
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
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.