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.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Kaburlasos, Vassilis G.; Papadakis, S. E.; Amanatiadis, Angelos: Binary image 2D shape learning and recognition based on lattice-computing (LC) techniques (2012)
- Khezeli, Yazdan Jamshidi; Nezamabadi-Pour, Hossein: Fuzzy lattice reasoning for pattern classification using a new positive valuation function (2012)
- Sussner, Peter; Esmi, Estevão L.; Villaverde, Ivan; Graña, Manuel: The Kosko subsethood fuzzy associative memory (KS-FAM): mathematical background and applications in computer vision (2012)
- Liu, Hongbing; Xiong, Shengwu; Fang, Zhixiang: FL-GrCCA: a granular computing classification algorithm based on fuzzy lattices (2011)