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
The Inconsistency in Rough Set Based Rule Generation
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Constraint Based Incremental Learning of Classification Rules
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Rule sets based bilevel decision model and algorithm
Expert Systems with Applications: An International Journal
An Incremental Approach for Inducing Knowledge from Dynamic Information Systems
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Temporal Dynamics in Information Tables
Fundamenta Informaticae - From Physics to Computer Science: to Gianpiero Cattaneo for his 70th birthday
Artificial Intelligence Review
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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.