Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Incremental Induction of Decision Trees
Machine Learning
IEEE Transactions on Knowledge and Data Engineering
Rule sets based bilevel decision model
ACSC '06 Proceedings of the 29th Australasian Computer Science Conference - Volume 48
An Incremental Approach for Inducing Knowledge from Dynamic Information Systems
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Research on rough set theory and applications in China
Transactions on rough sets VIII
The incremental method for fast computing the rough fuzzy approximations
Data & Knowledge Engineering
Incremental learning optimization on knowledge discovery in dynamic business intelligent systems
Journal of Global Optimization
Incremental attribute reduction based on elementary sets
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Neighborhood rough sets for dynamic data mining
International Journal of Intelligent Systems
Attribute reduction for dynamic data sets
Applied Soft Computing
Composite rough sets for dynamic data mining
Information Sciences: an International Journal
Updating attribute reduction in incomplete decision systems with the variation of attribute set
International Journal of Approximate Reasoning
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As a special way in which the human brain is learning new knowledge, incremental learning is an important topic in AI. It is an object of many AI researchers to find an algorithm that can learn new knowledge quickly, based on original knowledge learned before, and in such way that the knowledge it acquires is efficient in real use. In this paper, we develop a rough set and rule tree based incremental knowledge acquisition algorithm. It can learn from a domain data set incrementally. Our simulation results show that our algorithm can learn more quickly than classical rough set based knowledge acquisition algorithms, and the performance of knowledge learned by our algorithm can be the same as or even better than classical rough set based knowledge acquisition algorithms. Besides, the simulation results also show that our algorithm outperforms ID4 in many aspects.