On Objective-Based Roughc-Regression

2015 ◽  
Vol 19 (1) ◽  
pp. 36-42
Author(s):  
Akira Sugawara ◽  
◽  
Yasunori Endo ◽  
Naohiko Kinoshita ◽  
Keyword(s):  
Pattern Recognition ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Clustering Algorithm ◽  
Rough Set Theory ◽  
Pattern Recognition Method ◽  
Clustering Methods ◽  
Recognition Method ◽  
Numerical Examples

The pattern recognition method of clustering is a technique automatically classifying data into clusters. Among clustering methods,c-regression based on fuzzy set theory, called Fuzzyc-Regression (FCR), is proposed to get a linear dataset structure. The most recent clustering is based on rough set theory called rough clustering, which is less descriptive than fuzzy clustering. A typical rough clustering algorithm is Roughk-Regression (RKR). However, RKR has problems because it depends on initial values and has no optimum index, so we do not know whether a clustering result will be optimal. This paper proposes Roughc-Regression (RCR) based on the optimization of an objective function and demonstrates its effectiveness through numerical examples.

Research on Information Technology with Character Pattern Recognition Method Based on Rough Set Theory

2014 ◽  
Vol 886 ◽  
pp. 519-523 ◽  
Author(s):  
Yong Li Liu
Keyword(s):  
Information Technology ◽  
Pattern Recognition ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Pattern Recognition Method ◽  
Two Dimensional ◽  
Recognition Method ◽  
Two Dimensional Image

Character Pattern recognition is widely used in the information technology field. This paper proposes a method of character pattern recognition based on rough set theory. By giving the characters two dimensional image, defining the location of the characteristic and abstracting the characteristic value, the knowledge table and table reduction can be ascertained. Then the decision rules can be deduced. Through the simulation of 26 English alphabets, the results illustrate this methods validity and correctness.

Rough Set and K-Means Clustering Algorithm Based on Ant Colony Algorithm

2013 ◽  
Vol 392 ◽  
pp. 837-840 ◽  
Author(s):  
Ying Meng ◽  
Ke Luo ◽  
Jian Hua Liu
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Ant Colony Algorithm ◽  
Clustering Algorithm ◽  
Simulated Annealing Algorithm ◽  
Rough Set Theory ◽  
Boundary Objects ◽  
Ant Colony ◽  
Clustering Methods ◽  
Cluster Number

Traditional K-means clustering methods have great attachment to the selection of the initial value and easily get into the local extreme value. This paper proposes a synthetic clustering algorithm of rough set and K-means based on Ant colony algorithm. While the rough set theory presents processing method of uncertain boundary objects, Ant colony algorithm is a bionic optimization algorithm, which has strong robustness, easily with other method unifies, solving efficiency higher characteristic.. Therefore, the K-means algorithm based on Ant colony algorithm in this paper combines rough set theory with simulated annealing algorithm and K-means, in which K means cluster number and initial cluster centers can be obtained dynamically with the principle of maximum minimum, and processing boundary objects with upper and lower approximation of rough set theory. Finally, the UCIs Iris set is used to test the algorithm. The experimental results show that the algorithm has higher accuracy rate, faster execution time and more stable performance.

A Novel Emotion Recognition Method Based on Ensemble Learning and Rough Set Theory

2011 ◽  
Vol 5 (3) ◽  
pp. 61-72 ◽  
Author(s):  
Yong Yang ◽  
Guoyin Wang
Keyword(s):  
Emotion Recognition ◽  
Ensemble Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Classical Method ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Simulation Experiments ◽  
Audio Information

Emotion recognition is a very hot topic, which is related with computer science, psychology, artificial intelligence, etc. It is always performed on facial or audio information with classical method such as ANN, fuzzy set, SVM, HMM, etc. Ensemble learning theory is a novelty in machine learning and ensemble method is proved an effective pattern recognition method. In this paper, a novel ensemble learning method is proposed, which is based on selective ensemble feature selection and rough set theory. This method can meet the tradeoff between accuracy and diversity of base classifiers. Moreover, the proposed method is taken as an emotion recognition method and proved to be effective according to the simulation experiments.

A Novel Emotion Recognition Method Based on Ensemble Learning and Rough Set Theory

2012 ◽  
pp. 128-139 ◽  
Author(s):  
Yong Yang ◽  
Guoyin Wang
Keyword(s):  
Emotion Recognition ◽  
Ensemble Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Classical Method ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Selective Ensemble ◽  
Audio Information

Emotion recognition is a very hot topic, which is related with computer science, psychology, artificial intelligence, etc. It is always performed on facial or audio information with classical method such as ANN, fuzzy set, SVM, HMM, etc. Ensemble learning theory is a novelty in machine learning and ensemble method is proved an effective pattern recognition method. In this paper, a novel ensemble learning method is proposed, which is based on selective ensemble feature selection and rough set theory. This method can meet the tradeoff between accuracy and diversity of base classifiers. Moreover, the proposed method is taken as an emotion recognition method and proved to be effective according to the simulation experiments.

Imprecise Ontology Merging Framework Design

2011 ◽  
Vol 135-136 ◽  
pp. 578-584
Author(s):  
Guan Yu Li ◽  
Yan Zhao ◽  
Hai Yan Li
Keyword(s):  
Semantic Web ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Rough Set Theory ◽  
Distributed Environment ◽  
Ontology Merging ◽  
Web Environment ◽  
Framework Design ◽  
Efficient Measure

Precision is selected unwillingly by human being when dealing with imprecise objects because of the limitation of human cognitive ability, which deviates from the substance of the processed object when it gets the feasible way of solution. Nowadays, in terms of the research in the Ontology and the Semantic Web, the time for the transformation from the “precise phase” to the “imprecise phase” is ripe. The interoperability among ontologies is seriously blocked by the heterogeneity of ontologies constructed under distributed environment. In this case, Ontology merging in the same domain is the most effective method to solve ontology heterogeneity. Firstly, the improved fuzziness and the R-improved roughness are respectively defined and verified as the more efficient measure way for the fuzziness and roughness. Secondly, a composite appraisal method of fuzzy-rough relevancy in combination of the fuzzy set theory and the rough set theory is proposed, which can serve as the basis of the inquiry and reasoning of the imprecise ontology, the transformation reference of the fuzzy roughness set or the rough fuzziness set. Lastly, by employing semantic bridge generator and conflict processor, a novel multiple-mapping-based imprecise ontology merging framework is proposed. The example verification reveals that both the imprecise ontology merging efficiency can be improved and the merging source imprecise ontologies into object imprecise ontology can be done automatically under the semantic web environment.

scholarly journals Topology in the Alternative Set Theory and Rough Sets via Fuzzy Type Theory

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 432 ◽  
Author(s):  
Vilém Novák
Keyword(s):  
Fuzzy Logic ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Rough Sets ◽  
Type Theory ◽  
Rough Set Theory ◽  
Basic Concepts ◽  
Alternative Set

In this paper, we will visit Rough Set Theory and the Alternative Set Theory (AST) and elaborate a few selected concepts of them using the means of higher-order fuzzy logic (this is usually called Fuzzy Type Theory). We will show that the basic notions of rough set theory have already been included in AST. Using fuzzy type theory, we generalize basic concepts of rough set theory and the topological concepts of AST to become the concepts of the fuzzy set theory. We will give mostly syntactic proofs of the main properties and relations among all the considered concepts, thus showing that they are universally valid.

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