Principles and practice of information theory
Principles and practice of information theory
Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
A Method for Attribute Selection in Inductive Learning Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Features of the ORION object-oriented database system
Object-oriented concepts, databases, and applications
Object-oriented database systems
Proceedings of the seventh ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Incremental Induction of Decision Trees
Machine Learning
Machine Learning
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Queries in Object-Oriented Databases
Proceedings of the Fourth International Conference on Data Engineering
An Attribute-Oriented Approach for Learning Classification Rules from Relational Databases
Proceedings of the Sixth International Conference on Data Engineering
A Method of Processing Unknown Attribute Values by ID3
ICCI '92 Proceedings of the Fourth International Conference on Computing and Information: Computing and Information
Research Frontiers in Object Technology
Information Systems Frontiers
State Space Segmentation for Acquisition of Agent Behavior
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Fuzzy Q-Learning with the modified fuzzy ART neural network
Web Intelligence and Agent Systems
State space segmentation for acquisition of agent behavior
Web Intelligence and Agent Systems
A new logic correlation rule for HIV-1 protease mutation
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
The data-driven characteristic of Version Space 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 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, AGE, taking advantages of Version Space and ID3 to learn rules from object-oriented databases (OODB) 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 to the number of training instances.