Journal Article10.11591/TELKOMNIKA.V11I7.2802
Two-step Classification Algorithm Based on Decision-Theoretic Rough Set Theory
Jun Wang,Yulong Xu,Weidong Yu +2 more
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TL;DR: A two-step classification algorithm is proposed that decreases the range of negative domain and employs a two-steps strategy in classification and can gain high accuracy and low loss.
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Abstract: This paper introduces rough set theory and decision-theoretic rough set theory. Then based on the latter, a two-step classification algorithm is proposed. Compared with primitive DTRST algorithms, our method decreases the range of negative domain and employs a two-steps strategy in classification. New samples and unknown samples can be estimated whether it belongs to the negative domain when they are found. Then, fewer wrong samples will be classified in negative domain. Therefore, error rate and loss of classification is lowered. Compared with traditional information filtering methods, such as Naive Bayes algorithm and primitive DTRST algorithm, the proposed method can gain high accuracy and low loss. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2802
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Citations
Bayesian Network Structure Learning Based On Rough Set and Mutual Information
Zuhong Feng,Xiujuan Gao,Long Wang +2 more
- 06 Oct 2013
TL;DR: An algorithm of attribute reduction based on rough set is introduced that can effectively reduce the dimension of attributes and quickly determine the network structure using mutual information for Bayesian network structure learning.
1
•Journal Article
Extension of rough set under incomplete information systems
TL;DR: A new extension of rough set based on limited tolerance relation is presented, which combines tolerance relation, non-symmetric similarity relation, and valued tolerance relation.
References
A decision theoretic framework for approximating concepts
Yiyu Yao,S. K. M. Wong +1 more
TL;DR: This paper shows that if a given concept is approximated by one set, the same result given by the α-cut in the fuzzy set theory is obtained, and can derive both the algebraic and probabilistic rough set approximations.
Decision-theoretic rough set models
Yiyu Yao
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TL;DR: It is shown that the decision-theoretic models need to consider additional issues in probabilistic rough set models.
Extension of rough set under incomplete information systems
Guoyin Wang
- 07 Aug 2002
TL;DR: A new extension of rough set theory is developed that is based on a limited tolerance relation based on an indiscernibility relation that is a kind of equivalent relation.
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Rough Sets Theory and Its Application
TL;DR: The basie concepts for rough set theory, including equivalent relation, upper\lower approximation and reduction, and some applications of rough sets theory in areas like ANN, machine learning、 data mining etc are also discussed.
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3DM: Domain-oriented Data-driven Data Mining
Guoyin Wang,Yan Wang +1 more
TL;DR: A domain-oriented data-driven data mining (3DM) model based on a conceptual data mining model is proposed and some data- driven data mining algorithms are also proposed to show the validity of this model, e.g., the data- Driven default rule generation algorithm,Data-driven decision tree pre-pruning algorithm and data-linked knowledge acquisition from concept lattice.
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