DocumentCode :
2225052
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
Volume :
4
fYear :
2010
fDate :
20-22 Aug. 2010
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;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on
Conference_Location :
Chengdu
ISSN :
2154-7491
Print_ISBN :
978-1-4244-6539-2
Type :
conf
DOI :
10.1109/ICACTE.2010.5579408
Filename :
5579408
Link To Document :
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