Results 1 to 10 of about 80,260 (264)

An Integration of Cloud Transform and Rough Set Theory to Induction of Decision Trees

Fundamenta Informaticae, 2009
Decision trees are one of the most popular data-mining techniques for knowledge discovery. Many approaches for induction of decision trees often deal with the continuous data and missing values in information systems. However, they do not perform well in real situations.
Jing Song, Tianrui Li 0001, Da Ruan 0001
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Construction of Decision Tree Based on Rough Sets Theory

Advanced Materials Research, 2012
In the process of constructing decision trees, the selecting criteria of classification attributes will directly affect the classification results. Here we presented the classification contribution function (CCF), a new concept based on rough sets theory, which is regarded as the criteria for choosing attributes in the core of attributes.
Zhi Jun Lei, Lin Li Wu
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A Framework for a Decision Tree Learning Algorithm with Rough Set Theory

2015
In this paper, we improve the conventional decision tree learning algorithm using rough set theory. First, our approach gets the upper approximate for each class. Next, it generates the decision tree from each upper approximate. Each decision tree shows whether the data item is in this class or not. Our approach classifies the unlabeled data item using
Masaki Kurematsu, Jun Hakura, Hamido Fujita   +2 more
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An Algorithm for Decision Tree Construction Based on Rough Set Theory

2008 International Conference on Computer Science and Information Technology, 2008
In this paper, a novel and effective algorithm is introdcued for constructing decision tree. First of all, the knowledge dependence in rough set theory is used to reduce the test attribute set of decision tree, that is, the test attribute space is optimized and hence the attributes which are not correlated with the decision information are deleted ...
Cuiru Wang, Fangfang Ou
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A New Decision Tree Algorithm Based on Rough Set Theory

2009 Asia-Pacific Conference on Information Processing, 2009
Decision tree algorithm has been widely used to classify numeric and categorical attributes. Lots of approaches were suggested in order to induce decision trees. ID3 (Quinlan, 1986), as a heuristic algorithm, is very classic and popular in the induction of decision trees.
Baoshi Ding, Yongqing Zheng, Shaoyu Zang
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Data-driven decision tree learning algorithm based on rough set theory

Proceedings of the 2005 International Conference on Active Media Technology, 2005. (AMT 2005)., 2005
Decision tree pre-pruning is an effective method to solve the over-fitting problem in decision tree learning process. However, it is difficult to estimate the exact time to stop the growing process of a decision tree, which limits the developments and applications of this method.
Desheng Yin, Guoyin Wang 0001, Yu Wu 0001   +2 more
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A Contribution to Decision Tree Construction Based on Rough Set Theory

2004
In this paper, the algorithm of building a decision tree is introduced by comparing the information gain or entropy. The produced process of univariate decision tree is given as an example. According to rough sets theory, the method of constructing multivariate decision tree is discussed.
Xumin Liu, Houkuan Huang, Weixiang Xu
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Induction of Decision Trees Based on the Rough Set Theory

1998
This paper aimed at two following objectives. One was the introduction of a new measure (R-measure) of dependency between groups of attributes in a data set, inspired by the notion of dependency of attribute in the rough set theory. The second was the application of this measure to the problem of attribute selection in decision tree induction, and an ...
Tu Bao Ho, Trong Dung Nguyen, Masayuki Kimura   +2 more
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An Optimized Parallel Decision Tree Model Based on Rough Set Theory

2008
This paper presents an optimized parallel decision tree model based on rough set theory, first the model divides global database into subsets, then using the intuitive classification ability of decision tree to learn the rules in each subset, at last merge each subset's rule set to obtain the global rule set.
Xiaowang Ye, Zhijing Liu
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A fast algorithm for attribute reduction based on Trie tree and rough set theory

SPIE Proceedings, 2013
Attribute reduction is an important issue in rough set theory. Many efficient algorithms have been proposed, however, few of them can process huge data sets quickly. In this paper, combining the Trie tree, the algorithms for computing positive region of decision table are proposed.
Feng Hu, Xiao-yan Wang, Chuan-jiang Luo
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