Learning cross-level certain and possible rules by rough sets

Abstract

Machine learning can extract desired knowledge and ease the development bottleneck in building expert systems. Among the proposed approaches, deriving rules from training examples is the most common. Given a set of examples, a learning program tries to induce rules that describe each class. Recently, the rough-set theory has been widely used in dealing with data classification problems. Most of the previous studies on rough sets focused on deriving certain rules and possible rules on the single concept level. Data with hierarchical attribute values are, however, commonly seen in real-world applications. This paper thus attempts to propose a new learning algorithm based on rough sets to find cross-level certain and possible rules from training data with hierarchical attribute values. It is more complex than learning rules from training examples with single-level values, but may derive more general knowledge from data. Boundary approximations, instead of upper approximations, are used to find possible rules, thus reducing some subsumption checking. Some pruning heuristics are also adopted in the proposed algorithm to avoid unnecessary search.