Fuzzy tolerance relation, fuzzy tolerance space and basis
Fuzzy Sets and Systems
Unsupervised feature selection using a neuro-fuzzy approach
Pattern Recognition Letters
Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Fuzzy-rough nearest neighbor algorithms in classification
Fuzzy Sets and Systems
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Generalized rough sets, entropy, and image ambiguity measures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
Fuzzy sets in pattern recognition and machine intelligence
Fuzzy Sets and Systems
A comparative study of fuzzy sets and rough sets
Information Sciences: an International Journal
Feature Selection Using f-Information Measures in Fuzzy Approximation Spaces
IEEE Transactions on Knowledge and Data Engineering
Fuzzy-rough sets for information measures and selection of relevant genes from microarray data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Nearest-neighbor guided evaluation of data reliability and its applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Fuzzy-rough nearest neighbour classification and prediction
Theoretical Computer Science
Fuzzy probabilistic approximation spaces and their information measures
IEEE Transactions on Fuzzy Systems
Fuzzy Rough Sets: The Forgotten Step
IEEE Transactions on Fuzzy Systems
Extending Data Reliability Measure to a Filter Approach for Soft Subspace Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incorporating logistic regression to decision-theoretic rough sets for classifications
International Journal of Approximate Reasoning
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Fuzzy-rough sets have enjoyed much attention in recent years as an effective way in which to extend rough set theory such that it can deal with real-valued data. More recently, fuzzy-rough sets have been employed for the task of classification. This has led to the development of approaches such as fuzzy-rough nearest-neighbour (FRNN) and its extension based on vaguely-quantified rough sets (VQNN). These methods perform well and experimental evaluation demonstrates that VQNN in particular is very effective for dealing with data in the presence of noise. In this paper, the underlying mechanisms of FRNN and VQNN are investigated and analysed. The theoretical proof and empirical evaluation show that the resulting classification of FRNN and VQNN depends only upon the highest similarity and greatest summation of the similarities of each class, respectively. This fact is exploited in order to formulate the novel methods proposed in this paper: similarity nearest-neighbour (SNN) and aggregated-similarity nearest-neighbour (ASNN). These two novel approaches are equivalent to FRNN and VQNN, but do not employ the concepts or framework of fuzzy-rough sets. Instead only fuzzy similarity is used. Experimental evaluation confirms the observation that these new methods maintain the classification performance of the existing advanced fuzzy-rough nearest-neighbour-based classifiers. In addition, the underlying mathematical foundation is simplified.