Identifying the Best Attributes for Decision Tree Learning Algorithms, Inspired by DNA Concepts, in Computer Science

摘要：

Decision trees are some kinds of learning structures which are used to provide approximations on the accurate solutions for new instances using learning data classifications. The core part in a Decision Tree Learning Algorithm is the approach taken in each phase for choosing better attributes. In this paper we tried to develop a new approach for selecting better attributes in training phase of a decision tree using DNA-base algorithms with lower complexity in arithmetic operators.

关键词： Decision Tree Attribute exponential function DNA Computer

作者: Ali Etemadi Mohammad-Mehdi Ebadzadeh Mehdi Eatemadi

作者单位: Islamic Azad University BandarLengeh BranchBandar Lengeh, Iran Amir Kabir University ofTechnology(AUT)Tehran, Iran Islamic Azad University Bandar Lengeh Branch Bandar Lengeh, Iran

会议类型: 国际会议

会议名称: 2010 3rd International Conference on Advanced Computer Theory and Engineering(2010年第三届先进计算机理论与工程国际会议 ICACTE 2010)

会议地点: 成都

会议语种:英文

页码: 1-5

在线出版日期: 2010-08-20（万方平台首次上网日期，不代表论文的发表时间）