International Journal of Computational Intelligence Research (IJCIR)

Volume 2, Number 3 (2006)


Feature subset selection in unsupervised learning via multiobjective optimization

Handl Julia, Knowles Joshua
Manchester Interdisciplinary Biocentre University of Manchester, U.K.


In this paper, the problem of unsupervised feature selection and its formulation as a multiobjective optimization problem are investigated. Two existing multiobjective methods from the literature are revisited and used as the basis for an algorithmic framework, encompassing both wrapper and filter methods of feature selection. A number of alternative algorithms implemented within this framework are then evaluated using an extensive data test suite; the main effect investigated is that of the choice of a primary objective function (a secondary objective function is used only to militate against an inherent cardinality bias affecting all methods of feature subset evaluation). Particular attention is paid in the study to high-dimensional data sets in which the number of features is much larger than the number of data items.