AN INCREMENTAL DECISION TREE LEARNING METHODOLOGY REGARDING ATTRIBUTES IN MEDICAL DATA MINING

摘要：

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.

关键词： Incremental learning Learning regarding attributes Decision tree Medical data mining

作者: SAM CHAO FAI WONG

作者单位: Faculty of Science and Technology, University of Macau, Taipa, Macau

会议类型: 国际会议

会议名称: 2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

会议地点: 保定

会议语种:英文

页码: 1694-1699

在线出版日期: 2009-07-12（万方平台首次上网日期，不代表论文的发表时间）