Preprint Article Version 1 This version is not peer-reviewed

Method of Generating Contexts based on Self-adaptive Differential Particle Swarm Using Local Topology for Multimodal Optimization in the Case of Multigranulation. A Case Study

Version 1 : Received: 10 January 2019 / Approved: 14 January 2019 / Online: 14 January 2019 (07:16:39 CET)

How to cite: Arias, D.; Filiberto, Y.; Bello, R. Method of Generating Contexts based on Self-adaptive Differential Particle Swarm Using Local Topology for Multimodal Optimization in the Case of Multigranulation. A Case Study. Preprints 2019, 2019010125 (doi: 10.20944/preprints201901.0125.v1). Arias, D.; Filiberto, Y.; Bello, R. Method of Generating Contexts based on Self-adaptive Differential Particle Swarm Using Local Topology for Multimodal Optimization in the Case of Multigranulation. A Case Study. Preprints 2019, 2019010125 (doi: 10.20944/preprints201901.0125.v1).

Abstract

Multigranulation is a new approach to the Rough Set Theory, where several separability relationships are used to obtain different granulations of the universe. The Multigranulation starts from the existence of different contexts or subsets of features to characterize the objects of the universe. In this paper, a method for the generation of contexts from the construction of similarity relations is proposed.The proposed solution was evaluated in an international database using the KNN classifier. It was also applied in the solution of a real problem in Civil Engineering specifically in Traffic Engineering, the contexts generated from the proposal used to determine the features of higher incidence in the service level of the road. The results achieved both in the international database and in the proposed application demonstrate the applicability of the proposed method.

Subject Areas

multigranulation; separability relationships; service level of the road

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