Results 241 to 250 of about 1,077,252 (284)

Decision Tree Construction from Knowledge Discovered by Rough Sets Theory

Proceedings of the … International Conference on Operational Research, 2008
Decision Tree Construction from Knowledge Discovered by Rough Sets ...
Vrbka, Jasna, Dalbelo Bašić, Bojana
openaire   +2 more sources

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.
Song, Jing, Li, Tianrui, Ruan, Da
openaire   +1 more source

NB Tree Based Intrusion Detection Technique Using Rough Set Theory Model

2019
Security is an important need for any organization to establish trust and consequently prevents misuse. Intrusion detection system (IDS) is a defense mechanism that strives to differentiate between legitimate and malicious activities with intent to secure the resource and henceforth establish trust. Due to this importance significant amount of research
Neha Gupta, Ritu Prasad, Praneet Saurabh, Bhupendra Verma   +3 more
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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
openaire   +1 more source

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 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
openaire   +1 more source

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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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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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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Axiomatic fuzzy set theory-based fuzzy oblique decision tree with dynamic mining fuzzy rules

Neural Computing and Applications, 2019
This 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, Huaguang Zhang, Shaoxin Sun, Xianchang Wang, Qiang He   +4 more
openaire   +1 more source