An efficient inductive learning method for object-oriented database using attribute entropy

Abstract

The data-driven characteristic of the Version Space rule-learning method works efficiently in memory even if the training set is enormous. However, the concept hierarchy of each attribute used to generalize/specialize the hypothesis of a specific/general (S/G) set is processed sequentially and instance by instance, which degrades its performance. As for ID3, the decision tree is generated from the order of attributes according to their entropies to reduce the number of attributes in some of the tree paths. Unlike Version Space, ID3 generates an extremely complex decision tree when the training set is enormous. Therefore, we propose a method called AGE (A_RCH+OG_L+ASE_, where ARCH=Automatic geneRation of Concept Hierarchies, OGL=Optimal Generalization Level, and ASE=Attribute Selection by Entropy), taking advantages of Version Space and ID3 to learn rules from object-oriented databases (OODBs) with the least number of learning features according to the entropy. By simulations, we found the performance of our learning algorithm is better than both Version Space and ID3. Furthermore, AGE's time complexity and space complexity are both linear with the number of training instances.