A New Granular Computing Model Based on Algebraic Structure

Chen, Linshu; Wang, Jiayang; Wang, Weicheng; Li, Li

doi:10.1049/cje.2018.09.006

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…= C, R c , R i , R o , B, Ω, Θ which supplies a powerful conceptual modeling methodology for granular systems [25]. Viewing the granule structure as an algebraic operator and granulating by a congruence relation, Chen tries to propose the algebraic quotient model for granular computing [21], [26]. But, the aforementioned research only try to introduce granule structure as topology and algebra respectively, and none of them discusses classification methods from the granular computing prospective.…”

Section: Granule Structure Based Classificationmentioning

confidence: 99%

“…But, granule structure is not considered in the above references of granular computing models and classification methods. Actually, the granules on a granularity are not mutually independent [21].…”

Section: Introductionmentioning

confidence: 99%

“…Supposing the granule structure as an algebra [24], [25], Wang attempts developing a granular algebra system as an open system consisting of inputs, internal relations and outputs. Viewing the granule structure as an algebraic operator [21], [26], Chen tries to propose the algebraic quotient model for granular computing. However, the aforementioned research only try to introduce granule structure as topology and algebra respectively, and none of them discusses attached classification methods from the granular computing prospective.…”

Section: Introductionmentioning

confidence: 99%

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A Granular Computing Based Classification Method From Algebraic Granule Structure

et al. 2021

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Classification, as one of the main task of machine learning, corresponds to the core work of granular computing, namely granulation. Most of granular computing models and related classification methods are uniquely classifying by granule features, but not considering granule structure, especially in information area with widespread application of algebraic structure. In this paper, we propose a granular computing based classification method from algebraic granule structure. First of all, to pre-process the original data in the algebraic granule structure area, we formulate the algebraic structure based granularity with granule structure of an algebraic binary operator. Then, we propose a novel granular computing based classification method as well as related classifying algorithm with congruence partitioning granules and homomorphicly projecting granule structure. Finally, compared with tolerance neighborhood model and quotient space model, we prove that the proposed classification method is much more effective and robust while classifying the algebraic structure based granularity. The proposed granular computing based classification method provides an approach for classifying algebraic structure based granularity, and combines granular computing theory and classification theory of machine learning.

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Section: Granule Structure Based Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%