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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 ...
Tsau Young Lin, Joseph Chi Chung Chan
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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, 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.
Lin Li Wu, Zhi Jun Lei
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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.
Da Ruan, Jing Song, Tianrui Li
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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, Masayuki Kimura, Trong Dung Nguyen   +2 more
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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.
Guoyin Wang, Yu Wu, De-Sheng Yin
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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.
Xiao Yan Wang, Chuan-jiang Luo, Feng Hu, Feng Hu   +3 more
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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.
Chunxue Wei, Baowei Song
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Modelling the effect of forest site conditions on the ecophysiological suitability of tree species: an approach based on fuzzy set theory

Computers and Electronics in Agriculture, 2000
Harald Vacik, Karl Hönninger, Manfred J. Lexer   +2 more
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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, 2015
Decision 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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