Fast Feature Selection Method for Continuous Attributes with Nominal Class

Feature selection has become a relevant pre-processing problem on knowledge discovery in databases, because of very large databases or because some attributes are expensive to obtain. There is a large number of diverse feature selection methods for databases with pure nominal data (attributes and class), or pure continuous data, but little work has been done for the case of continuous attributes with nominal class. Normally what we can do is perform discretization, and then apply some traditional feature selection method; however the results can vary greatly depending on the discretization method used. We propose a direct method for feature selection on continuous data with nominal class, inspired in the Shannon's entropy and an Information Gain measure. In the experiments that we realized, with synthetic and real databases, the proposed method has shown to be fast and to produce very competitive solutions with a small set of attributes