RELIEF-based Selection of Decision Rules

When constructing rule classifiers for pattern recognition and classification tasks we can induce only some small set of decision rules, such as a minimal cover that is sufficient for recognition of learning samples, a subset of rules satisfying requirements for example with respect to rule support or strength, or a complete set of rules. Once some set is inferred, another approach becomes available, that of filtering out a group of rules meeting some given criteria. The paper presents the latter methodology, where all decision rules on examples are generated within Dominance-Based Rough Set Approach and the process of filtering exploits a ranking of conditional attributes obtained through Relief algorithm. The procedures are applied in the domain of computational stylistics or stylometry, dedicated to analysis of linguistic styles observable in samples of writing.