scholarly journals Fuzzy-rough set based semi-supervised learning

2011 ◽  
Author(s):  
Neil Mac Parthalain ◽  
Richard Jensen
Keyword(s):  
Supervised Learning ◽  
Rough Set ◽  
Fuzzy Rough Set

A Semi-Supervised Learning Algorithm for Data Classification

2015 ◽  
Vol 29 (05) ◽  
pp. 1551007 ◽  
Author(s):  
Cheng-Chien Kuo ◽  
Horng-Lin Shieh
Keyword(s):  
Supervised Learning ◽  
Rough Set ◽  
Learning Algorithm ◽  
Data Classification ◽  
Recognition System ◽  
Unlabeled Data ◽  
Power Cable ◽  
Fuzzy Rough Set ◽  
Data Set ◽  
Defect Type

In this study, a semi-supervised learning algorithm for data classification and defect type recognition system for a 25 kV cross-linked polyethylene (XLPE) underground power cable joint is proposed. The proposed algorithm integrates the fuzzy-rough set and shared nearest neighbors (SNN) method for the assignment of labels to unlabeled data. As such, the data set is divided into two subsets: one is labeled and the other is unlabeled. The SNN is adopted for measuring the similarity between unlabeled data and labeled data subsets. Then, according to the levels of similarity, the fuzzy-rough set algorithm is adopted for assigning labels to the unlabeled data. A defect type recognition system XLPE cable classification problem is proposed in order to test the proposed algorithm. To demonstrate the performance of the proposed method, the proposed algorithm is applied to two well-known data sets. The experimental results show that the proposed algorithm can obtain outstanding levels of performance.

Bipolar fuzzy rough set model on two different universes and its application

2012 ◽  
Vol 35 ◽  
pp. 94-101 ◽  
Author(s):  
Hai-Long Yang ◽  
Sheng-Gang Li ◽  
Shouyang Wang ◽  
Jue Wang
Keyword(s):  
Rough Set ◽  
Fuzzy Rough Set

GENERALIZED FUZZY ROUGH SETS BY CONDITIONAL PROBABILITY RELATIONS

2002 ◽  
Vol 16 (07) ◽  
pp. 865-881 ◽  
Author(s):  
ROLLY INTAN ◽  
MASAO MUKAIDONO
Keyword(s):  
Conditional Probability ◽  
Rough Set ◽  
Real World ◽  
Rough Sets ◽  
Similarity Relation ◽  
Fuzzy Rough Set ◽  
Fuzzy Similarity ◽  
Fuzzy Similarity Relation ◽  
Real World Applications ◽  
The Universe

In 1982, Pawlak proposed the concept of rough sets with a practical purpose of representing indiscernibility of elements or objects in the presence of information systems. Even if it is easy to analyze, the rough set theory built on a partition induced by equivalence relation may not provide a realistic view of relationships between elements in real-world applications. Here, coverings of, or nonequivalence relations on, the universe can be considered to represent a more realistic model instead of a partition in which a generalized model of rough sets was proposed. In this paper, first a weak fuzzy similarity relation is introduced as a more realistic relation in representing the relationship between two elements of data in real-world applications. Fuzzy conditional probability relation is considered as a concrete example of the weak fuzzy similarity relation. Coverings of the universe is provided by fuzzy conditional probability relations. Generalized concepts of rough approximations and rough membership functions are proposed and defined based on coverings of the universe. Such generalization is considered as a kind of fuzzy rough set. A more generalized fuzzy rough set approximation of a given fuzzy set is proposed and discussed as an alternative to provide interval-value fuzzy sets. Their properties are examined.

Unsupervised fuzzy-rough set-based dimensionality reduction

2013 ◽  
Vol 229 ◽  
pp. 106-121 ◽  
Author(s):  
Neil Mac Parthaláin ◽  
Richard Jensen
Keyword(s):  
Dimensionality Reduction ◽  
Rough Set ◽  
Fuzzy Rough Set
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