AttributeNets: an incremental learning method for interpretable classification
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Research on rough set theory and applications in China
Transactions on rough sets VIII
Parallel reducts based on attribute significance
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Classification and decision based on parallel reducts and f-rough sets
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
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As a special way of human brains in 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 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.