Results 251 to 260 of about 1,076,762 (310)

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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Notice of Retraction: Algorithm of constructing decision tree based on rough set theory

2010 International Conference on Computer and Communication Technologies in Agriculture Engineering, 2010
Problem 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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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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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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A NOTE ON TREE DIAGRAMS, SET THEORY AND SYMBOLIC LOGIC

The Professional Geographer, 1966
Louis Hamill
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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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Using Rough Set Theory and Decision Trees to Diagnose Enterprise Distress – Consideration of Corporate Governance Variables

, 2014
This study discusses the key factors of financial distress warning models for companies using corporate governance variables and financial ratios as the research variables, sieving out influential variables based on the attribute simplification process of rough set theory (RST).
Fu Hsiang Chen, Der-Jang Chi, Chun-Yi Kuo   +2 more
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A New Method for Constructing Decision Tree Based on Rough Set Theory

2007 IEEE International Conference on Granular Computing (GRC 2007), 2007
Longjun Huang, Minghe Huang, Bin Guo, Zhiming Zhuang   +3 more
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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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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.
null Desheng Yin, null Guoyin Wang, null Yu Wu   +2 more
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