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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, Shaoyu Zang, Yongqing Zheng
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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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2007 International Conference on Intelligent Systems Applications to Power Systems, 2007 The fault detection and analysis for power transformer are the key measures to improve the security of power systems and the reliability of power supply. Due to the complicity of the power transformer structure and the variations in operating conditions, the occurrence of a fault inside power transformer is uncertain and random.
Guangyu Tu, Tong Wu, Z.Q. Bo, A. Klimek
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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.
Xu-min Liu, Wei-xiang Xu, Houkuan Huang
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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
Jun Hakura +2 more
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A NOTE ON TREE DIAGRAMS, SET THEORY AND SYMBOLIC LOGIC
The Professional Geographer, 1966 Louis Hamill
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Reliability Engineering & System Safety, 1992 Abstract Expert opinion is frequently used in the risk analysis of nuclear power plant systems to assess, in particular, the probabilities of rare events. However, this procedure is always accompanied by imprecision and uncertainty that characterise the experts judgment. Since the fuzzy set theory provides a framework for dealing with such judgmental
moon-hyun chun Chun, Moon Hyun +1 more
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An Intelligent Stock-Selecting System Based on Decision Tree Combining Rough Sets Theory
, 2013 This study presents a stock selective system by using hybrid models to look for sound financial companies that are really worth making investment in stock markets. The following are three main steps in this study: First, we utilize rough sets theory to sift out the core of the financial indicators affecting the ups and downs of a stock price.
Shou-Hsiung Cheng
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Multimedia Tools and Applications, 2016 Feature extraction is an essential task in the classification of high-resolution remote sensing images, with the primary technique being the object-oriented classification method. Current research describes object-oriented classification methods by using remote sensing data, wherein how to reduce the redundant feature information to achieve good ...
Yi Zeng +3 more
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