Feature Subset Selection in Unsupervised Learning via Multiobjective Optimization

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 methodsoffeatureselection. Anumberofalternativealgorithms implemented within this framework are then evaluated using an extensive data test suite; the main effect investigated is that of thechoiceofaprimaryobjectivefunction(asecondaryobjective function is used only to militate against an inherent cardinality bias affecting all methods of feature subset evaluation). Partic- ular attention is paid in the study to high-dimensional data sets in which the numberof features is much largerthan the number