An incremental decision tree learning methodology regarding attributes in medical data mining

• Published in: 2009 International Conference on Machine Learning and Cybernetics Vol. 3; pp. 1694 - 1699
• Main Authors: Chao, S., Fai Wong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2009
• Subjects: Chaos; Classification tree analysis; Concrete; Cybernetics; Data mining; Decision tree; Decision trees; Incremental learning; Learning regarding attributes; Learning systems; Machine learning; Machine learning algorithms; Medical data mining; Medical diagnostic imaging
• ISBN: 9781424437023; 1424437024
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2009.5212333

Decision tree is one kind of inductive learning algorithms that offers an efficient and practical method for generalizing classification rules from previous concrete cases that already solved by domain experts. It is considered attractive for many real life applications, mostly due to its interpretability. Recently, many researches have been reported to endow decision trees with incremental learning ability, which is able to address the learning task with a stream of training instances. However, there are few literatures discussing the algorithms with incremental learning ability regarding the new attributes. In this paper, i + Learning (Intelligent, Incremental and Interactive Learning) theory is proposed to complement the traditional incremental decision tree learning algorithms by concerning new available attributes in addition to the new incoming instances. The experimental results reveal that i + Learning method offers the promise of making decision trees a more powerful, flexible, accurate and valuable paradigm, especially in medical data mining community.