On Combining Discretisation Parameters and Attribute Ranking for Selection of Decision Rules

摘要

The paper describes research on filtering decision rules with continuous and discretised condition attributes while combining characteristics of these attributes returned from supervised discretisation with their ranking. Numbers of intervals required for partitioning of attributes values imposed their grouping into corresponding categories, and for each group separately ranking procedures with Relief algorithm were executed. Information about numbers of bins combined with ranking positions were next exploited for selection of rules induced within rough set approaches. Filtering rules was performed directly by their conditions, or by calculating defined measures based on attribute weights, returning shortened decision algorithms with at least the same or improved classification accuracy.