Feature selection ensemble

Many strategies have been exploited for the task of feature selection, in an effort to identify more compact and better quality feature subsets. Such techniques typically involve the use of an individual feature significance evaluation, or a measurement of feature subset consistency, that work together with a search algorithm in order to determine a quality subset. Feature selection ensemble aims to combine the outputs of multiple feature selectors, thereby producing a more robust result for the subsequent classifier learning tasks. In this paper, three novel implementations of the feature selection ensemble concept are introduced, generalising the ensemble approach so that it can be used in conjunction with many subset evaluation techniques, and search algorithms. A recently developed heuristic algorithm: harmony search is employed to demonstrate the approaches. Results of experimental comparative studies are reported in order to highlight the benefits of the present work. The paper ends with a proposal to extend the application of feature selection ensemble to aiding the development of biped robots (inspired by the authors’ involvement in the joint celebration of Olympic and the centenary of the birth of Alan Turing).