Pruning Decision Rules by Reduct-Based Weighting and Ranking of Features

Abstract

Methods and techniques of feature selection support expert domain knowledge in the search for attributes, which are the most important for a task. These approaches can also be used in the process of closer tailoring of the obtained solutions when dimensionality reduction is aimed not only at variables but also at learners. The paper reports on research where attribute rankings were employed to filter induced decision rules. The rankings were constructed through the proposed weighting factor based on the concept of decision reducts—a feature reduction mechanism embedded in the rough set theory. Classical rough sets operate only in discrete input space by indiscernibility relation. Replacing it with dominance enables processing real-valued data. Decision reducts were found for both numeric and discrete attributes, transformed by selected discretisation approaches. The calculated ranking scores were used to control the selection of decision rules. The performance of the resulting rule classifiers was observed for the entire range of rejected variables, for decision rules with conditions on continuous values, discretised conditions, and also inferred from discrete data. The predictive powers were analysed and compared to detect existing trends. The experiments show that for all variants of the rule sets, not only was dimensionality reduction possible, but also predictions were improved, which validated the proposed methodology.

Importance of Characteristic Features and Their Form for Data Exploration

“…Feature ranking is also referred to as feature weighting. It encompasses the evaluation of individual attributes through the allocation of weights determined by their relevance [7].…”