A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Feature selection with neural networks
Pattern Recognition Letters
Granular computing in neural networks
Granular computing
A novel approach to neuro-fuzzy classification
Neural Networks
A granular computing framework for self-organizing maps
Neurocomputing
Attribute selection with fuzzy decision reducts
Information Sciences: an International Journal
Generalized rough sets, entropy, and image ambiguity measures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Fuzzy rough granular neural networks, fuzzy granules, and classification
Theoretical Computer Science
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Range-Based Distance Measure for Microarray Expressions and a Fast Gene-Ordering Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Granular Neural Networks With Evolutionary Interval Learning
IEEE Transactions on Fuzzy Systems
Unsupervised feature evaluation: a neuro-fuzzy approach
IEEE Transactions on Neural Networks
Fuzzy rough granular self-organizing map and fuzzy rough entropy
Theoretical Computer Science
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A granular neural network for identifying salient features of data, based on the concepts of fuzzy set and a newly defined fuzzy rough set, is proposed. The formation of the network mainly involves an input vector, initial connection weights and a target value. Each feature of the data is normalized between 0 and 1 and used to develop granulation structures by a user defined @a-value. The input vector and the target value of the network are defined using granulation structures, based on the concept of fuzzy sets. The same granulation structures are also presented to a decision system. The decision system helps in extracting the domain knowledge about data in the form of dependency factors, using the notion of new fuzzy rough set. These dependency factors are assigned as the initial connection weights of the proposed network. It is then trained using minimization of a novel feature evaluation index in an unsupervised manner. The effectiveness of the proposed network, in evaluating selected features, is demonstrated on several real-life datasets. The results of FRGNN are found to be statistically more significant than related methods in 28 instances of 40 instances, i.e., 70% of instances, using the paired t-test.