Many search strategies have been exploited in implementing feature selection, in an effort to identify smaller and better subsets. Such work typically involves the use of heuristics in one form or another. In this paper two novel methods are presented by applying harmony search to feature selection. In particular, it demonstrates the potential of utilising this search mechanism in combination with fuzzy-rough feature evaluation. The resulting techniques are compared with approaches that rely on hill-climbing, genetic algorithms and particle swarm optimisation.
|Title of host publication||2010 IEEE International Conference on Fuzzy Systems (FUZZ)|
|Number of pages||7|
|Publication status||Published - Jul 2010|