Learning from Hierarchical Attribute Values by Rough Sets

Hong, Tzung-Pei; Lin, Chun-E; Lin, Jiann-Horng; Wang, Shyue-Liang

doi:10.1007/978-3-540-44999-7_53

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Deriving rules on multiple concept levels may thus lead to the discovery of more general and important knowledge from data. This paper thus extends our previous approach to deal with the problem of producing a set of cross-level maximally general fuzzy certain and possible rules from examples with hierarchical and quantitative attributes [5]. Some pruning heuristics are adopted in the proposed algorithm to avoid unnecessary search.…”

Section: Introductionmentioning

confidence: 89%

Learning with Hierarchical Quantitative Attributes by Fuzzy Rough Sets

Hong¹,

Liou²,

Wang³

2006

Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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This paper proposes an approach to deal with the problem of producing a set of cross-level fuzzy certain and possible rules from examples with hierarchical and quantitative attributes. The proposed approach combines the rough-set theory and the fuzzy-set theory to learn. Some pruning heuristics are adopted in the proposed algorithm to avoid unnecessary search. A simple example is also given to illustrate the proposed approach.

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Section: Introductionmentioning

confidence: 89%

Learning with Hierarchical Quantitative Attributes by Fuzzy Rough Sets

Hong¹,

Liou²,

Wang³

2006

Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

Self Cite

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“…In 2003, Tzung-Pei Hong et al proposed a new learning algorithm based on rough sets to find cross-level certain and possible rules from training data with hierarchical attribute values, which is more complex than learning rules from training examples with single-level values, but may derive more general knowledge from data [8]. In 2009, Tzung-Pei Hong et al extended their previous approach to deal with the problem of producing a set of cross-level maximally general fuzzy certain and possible rules from examples with hierarchical and quantitative attributes, which combines the rough-set theory and the fuzzy-set theory to learn [9].…”

Section: The Methods Of Fuzzy Comprehensive Evaluationmentioning

confidence: 99%

An Algorithm Combining Random Forest Classification and Fuzzy Comprehensive Evaluation

Zhu¹,

Zhu²,

Huang³

et al. 2022

Journal of Internet Technology

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<p>Random forest algorithm is a common classification method. However, if the weights of many attributes in a data set are not same or close to each other, the direct use of this algorithm for data training will lead to the neglect of the interrelationships between these attributes, and it is difficult to reflect the differences brought by different weights of different attributes. Worse, if the number of attributes in the data set is relatively large, many attributes will be given very little weight when normalization is satisfied, which will also lead to information loss. All of these will have a negative impact on the final result. To solve these problems, this paper proposes an algorithm combining random forest classification and fuzzy comprehensive evaluation, which not only take into account the correlation between attributes in data training, but also retain the information in the original data set to the maximun. At the same time, this algorithm significantly improves the accuracy of random forest training results.</p> <p> </p>

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A Novel Approach Based on Fuzzy Rough Sets for Web Query System

Han

Liu

2010

Advances in Intelligent and Soft Computing

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Learning from Hierarchical Attribute Values by Rough Sets

Cited by 4 publications

References 8 publications

Learning with Hierarchical Quantitative Attributes by Fuzzy Rough Sets

Learning with Hierarchical Quantitative Attributes by Fuzzy Rough Sets

An Algorithm Combining Random Forest Classification and Fuzzy Comprehensive Evaluation

A Novel Approach Based on Fuzzy Rough Sets for Web Query System

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