Proceedings Article10.1109/FSKD.2010.5569347
Multi-relational Bayesian Classification Algorithm with Rough Set
Chunying Zhang,Jing Wang +1 more
- 09 Sep 2010
- Vol. 4, pp 1565-1568
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TL;DR: A Multi-relational Bayesian Classification Algorithm with Rough Set is proposed, which improves the accuracy rate and the running rate and a tuple ID propagation approach is used to solve directly the association rule mining problem with multiple database relations.
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Abstract: A Multi-relational Bayesian Classification Algorithm with Rough Set is proposed in this paper. The concept of relational graph used to dynamic choice associative table associated with the target table, and a tuple ID propagation approach is used to solve directly the association rule mining problem with multiple database relations, and the concept of Core in Rough Set is introduced, simplify the associative table. Compared with the traditional algorithm,it improves the accuracy rate. Experimental results show that its running rate is much higher than that of Bayesian Classification Algorithm and Graph_NB Algorithm.
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Citations
Dimensionality reduction in data summarization approach to learning relational data
Chung Seng Kheau,Rayner Alfred,Lau Hui Keng +2 more
- 18 Mar 2013
TL;DR: The effects of discretizing the magnitude of terms computed and applying a feature selection process that reduces the cardinalities of attributes of the relational datasets on the predictive accuracy of the overall classification task are investigated.
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A Weighted Relational Classification Algorithm Based on Rough Set
Fu Jinghong,Zhang Chunying,Wang Jing,Tian Fang +3 more
- 12 Mar 2011
TL;DR: A Weighted Relational Classification Algorithm Based on Rough Set is proposed in this paper and experiments have proved that new classifier has good classification performance.
Neuro-rough Sets for Modeling Conflict between China and Its Neighboring Countries
Xinjian Qiang,Guojian Cheng,Hong Xiao +2 more
- 15 Jun 2014
TL;DR: A neuro-rough model is introduced and extended to a probabilistic domain using a Bayesian framework, trained using a Markov Chain Monte Carlo simulation and the Metropolis algorithms to model interstate conflict between China and its neighboring countries.
1
Neuro-Rough Sets for Modeling Interstate Conflict
Tshilidzi Marwala,Monica Lagazio +1 more
- 01 Jan 2011
TL;DR: This chapter investigated a neuro-rough model –a combination of a Multi-Layered Perceptron (MLP) neural network with rough set theory– for the modeling of interstate conflict and found it to combine the accuracy of the Bayesian MLP model with the transparency of the rough set model.
1
Research of Leaf Quality Based on Snowflake Theory
TL;DR: To study the leaves quality, there were three parts in this paper, and snowflake model theory was proposed, which is high similarity between snow structure and tree structure, and the formation of the branch copies the exterior characteristics of the backbone.
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•Journal Article
An New Multi-Relational Association Rule Mining Algorithm with User's Guidance
TL;DR: A multi-relational association rule mining algorithm with guidance of user with much higher running rate is proposed, which improves the accuracy rate and supports multi- Relational database directly, so itsRunning rate is much higher than that of the ILP based multi-Relational associationRule mining methods.
1
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