Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Neighborhood systems and relational databases
CSC '88 Proceedings of the 1988 ACM sixteenth annual conference on Computer science
Fuzzy lattice neurocomputing (FLN) models
Neural Networks
Data Mining and Machine Oriented Modeling: A Granular Computing Approach
Applied Intelligence
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Towards a Unified Modeling and Knowledge-Representation based on Lattice Theory: Computational Intelligence and Soft Computing Applications (Studies in Computational Intelligence)
Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation
International Journal of Approximate Reasoning
A general framework for fuzzy morphological associative memories
Fuzzy Sets and Systems
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Lattice Independent Component Analysis for fMRI Analysis
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
An introduction to morphological perceptrons with competitive learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A lattice computing approach for on-line fMRI analysis
Image and Vision Computing
IEEE Transactions on Neural Networks
Fuzzy lattice reasoning for pattern classification using a new positive valuation function
Advances in Fuzzy Systems
Quantale-based autoassociative memories with an application to the storage of color images
Pattern Recognition Letters
Multi-argument fuzzy measures on lattices of fuzzy sets
Knowledge-Based Systems
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Defining a relation between granules and computing ever-changing granules are two important issues in granular computing. In view of this, this work proposes a partial order relation and lattice computing, respectively, for dealing with the aforementioned issues. A fuzzy lattice granular computing classification algorithm, or FL-GrCCA for short, is proposed here in the framework of fuzzy lattices. Algorithm FL-GrCCA computes a fuzzy inclusion relation between granules by using an inclusion measure function based on both a nonlinear positive valuation function, namely arctan, and an isomorphic mapping between lattices. Changeable classification granules are computed with a dilation operator using, conditionally, both the fuzzy inclusion relation between two granules and the size of a dilated granule. We compare the performance of FL-GrCCA with the performance of popular classification algorithms, including support vector machines (SVMs) and the fuzzy lattice reasoning (FLR) classifier, for a number of two-class problems and multi-class problems. Our computational experiments showed that FL-GrCCA can both speed up training and achieve comparable generalization performance.