CompactLEM2: A Scalable Rough Set based Knowledge Acquisition Method that Generates Small Number of Short Rules

The complexity of knowledge plays an important role in the success of any types of knowledge acquisition algorithms performing on large-scale database. LERS (Learning from examples based on rough sets) system is a rule based knowledge acquisition system that is characterized by excellent accuracy, but the complexity of generated rule set is not taken into account. This may cause interpretation problems for human and the classification knowledge may overfit training data. In this paper, CompactLEM2 is proposed as a scalable knowledge acquisition method that extracts rule set with easily understood rule forms, i.e., small size of rule set and short rule forms, without sacrificing classification accuracy. The main advantage of CompactLEM2 is its high efficiency. It can also produce compact rule set that fully or approximately describes classifications of given examples. We theoretically and experimentally show that CompactLEM2 exhibits log-linear asymptotic complexity with the number of training examples in most cases. We also present an example to illustrate characteristics of this algorithm. Finally, the capabilities of our method are demonstrated on eleven datasets. Experimental results are encouraging, and show that the length of extracted rule forms are short, and size of rule set is small, keeping the same level of classification accuracy of other rule acquisition methods in LERS system.