Rule induction using rough sets reducts as filter for selecting features: an empirical comparison with other filters.
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The Feature Subset Selection is an important problem within the Machine Learning area where the learning algorithm is faced with the. problem of selecting relevant features while ignoring the rest. Another important problem within this area is the complexity of the knowledge acquired (hypotheses) though rules induction. Rough Sets Theory is a mathematical tool to deal with vagueness and uncertainty information, One of the main features of this approach are the reducts, which is a minimal feature set that preserves the ability to discern each object from the others. This work presents in detail several experiments, results and comparisons using Rough Sets Reducts and other Filters for feature subset selection and rule induction. The purpose of this work is to investigate the reduction of the complexity of the rules induced in terms of the Feature Subset Selection problem, considering as measure of rules complexity the number of rules induced. All the experiments where run on natural datasets, most of them obtained from the UCI Irvine Repository.