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A Contribution to Decision Tree Construction Based on Rough Set Theory
2004In 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
1998This 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 +2 more
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An Optimized Parallel Decision Tree Model Based on Rough Set Theory
2008This 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, 2013Attribute 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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Notice of Retraction: Algorithm of constructing decision tree based on rough set theory
2010 International Conference on Computer and Communication Technologies in Agriculture Engineering, 2010Problem of classification is the main research target of many algorithms in machine learning and data mining. Of all the algorithms, decision tree is more preferred by researchers due to its clarity and readability. Attribute of little value domain is the important feature of training dataset of decision trees.
null Baowei Song, null Chunxue Wei
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Parameter trees based on soft set theory and their similarity measures
Soft Computing, 2022Faruk Karaaslan, Naim Çagman
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Decision Tree Construction from Knowledge Discovered by Rough Sets Theory
Proceedings of the … International Conference on Operational Research, 2008Decision Tree Construction from Knowledge Discovered by Rough Sets ...
Vrbka, Jasna, Dalbelo Bašić, Bojana
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The Based on Rough Set Theory Development of Decision Tree after Redundant Dimensional Reduction
2015 Fifth International Conference on Advanced Computing & Communication Technologies, 2015Decision tree technologists have been examined to be a helpful way to find out the human decision making within a host. Decision tree performs variable screening or feature selection. It requires relatively lesser effort from the users for the preparation of the data.
Priya Pal, Deepak Motwani
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Axiomatic fuzzy set theory-based fuzzy oblique decision tree with dynamic mining fuzzy rules
Neural Computing and Applications, 2019This paper proposes a novel classification technology—fuzzy rule-based oblique decision tree (FRODT). The neighborhood rough sets-based FAST feature selection (NRS_FS_FAST) is first introduced to reduce attributes. In the axiomatic fuzzy set theory framework, the fuzzy rule extraction algorithm is then proposed to dynamically extract fuzzy rules.
Yuliang Cai +4 more
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An Improved Decision Tree Algorithm Using Rough Set Theory in Clinical Decision Support System
2012In the Clinical Decision Support System (CDSS), over-fitting phenomenon may appear when decision tree algorithm was used. For this problem, this paper will make use of the Rough Set theory to the training set for attribute reduction, the decision tree built by using the decision tree algorithm was used to predict the test data. In this paper, 46 copies
Qingshan Li, Jian'guo Zhang, Hua Chu
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