Dual problems: Attribute selection and example selection

Abstract

We proposed an alternative methodology, Dual AE, for attribute selection and example selection. The Dual AE is designed to increase the accuracy and efficiency of attribute selection and example selection. The central idea behind the Dual AE is to generate the attribute candidates by "Attribute candidate generation" after the data were cleaned in "Data pre-processing module". All the candidates are weighted in the "Attribute weighting" so that the most informative attribute set can be selected in "Clustering". The accuracy of the selected attribute set is verified in the last module, "Validation". To demonstrate the Dual AE method, we have implemented it on the prototypical data set. In this paper we present the intuitions behind the design of Dual AE and some experimental results.