NANO: A New Supervised Algorithm for Feature Selection with Discretization

Abstract

Discretization turns numeric attributes into discrete ones. Feature selection eliminates some irrelevant and/or redundant attributes. Data discretization and feature selection are two important tasks that performed prior to the learning phase of data mining algorithms and significantly reduces the processing effort of the learning algorithm. In this paper, we present a new algorithm, called Nano, that can perform simultaneously data discretization and feature selection. In feature selection process irrelevant and redundant attributes as a measure of inconsistence are eliminated to determine the final number of intervals and to select features. The proposed Nano algorithm aims at keeping the minimal number of intervals with minimal inconsistency and establishes a tradeoff between these measures. The empirical results demonstrate that the proposed Nano algorithm is effective in feature selection and discretization of numeric and ordinal attributes.