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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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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 +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, Kwang-Il Ahn
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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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<title>Application of preprocessing filtering on Decision Tree C4.5 and rough set theory</title>
SPIE Proceedings, 2001 This paper compares two artificial intelligence methods: the Decision Tree C4.5 and Rough Set Theory on the stock market data. The Decision Tree C4.5 is reviewed with the Rough Set Theory. An enhanced window application is developed to facilitate the pre-processing filtering by introducing the feature (attribute) transformations, which allows users to ...
Joseph C. C. Chan, Tsau Young Lin
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Parameter trees based on soft set theory and their similarity measures
Soft Computing, 2022 Faruk Karaaslan, Naim Çağman
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Construction of Decision Tree Based on Rough Sets Theory
Advanced Materials Research, 2012In 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.
Lin Li Wu, Zhi Jun Lei
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A Transformation Tree Based on Extension Set Theory
Annals of Data ScienceXingsen Li +3 more
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An Integration of Cloud Transform and Rough Set Theory to Induction of Decision Trees
Fundamenta Informaticae, 2009Decision 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.
Da Ruan, Jing Song, Tianrui Li
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