Title :
Identifying the best attributes for Decision Tree Learning Algorithms, inspired by DNA concepts, in computer science
Author :
Etemadi, Ali ; Ebadzadeh, Mohammad-Mehdi ; Eatemadi, Mehdi
Author_Institution :
Lengeh Branch, Islamic Azad Univ. Bandar, Bandar Lengeh, Iran
Abstract :
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.
Keywords :
DNA; arithmetic; attribute grammars; computer science; decision trees; learning (artificial intelligence); mathematical operators; pattern classification; DNA concepts; arithmetic operators; attributes; computer science; decision tree; learning data classifications; Artificial neural networks; Computers; DNA; Rendering (computer graphics); Tin; Attribute; DNA Computer; Decision Tree; exponential function;
Conference_Titel :
Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-6539-2
DOI :
10.1109/ICACTE.2010.5579408