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A framework of granular-ball generation for classification via granularity tuning

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Abstract

In Granular-ball Computing (GbC), the radius of a granular-ball is usually defined as the maximum or average distance from all enclosed objects to the center. However, both methods face challenges in building a high-quality family of granular-balls for enhanced classification performance. The former often results in overlaps between heterogeneous granular-balls, and the latter may fail to cover all objects. This paper presents an effective way to define the radius with adaptive granularity tuning and explores the subsequent application of the constructed granular-balls in classifications. Specifically, we introduce the concept of generalized granular-ball, where the center and radius are calculated with weight factors. The general granular-balls are further enhanced through two steps. The first step removes their overlaps through the concepts of heterogeneous neighborhood and de-overlapping parameter, reducing the granular-balls to be tangent or separate to each other. The second step further refines the radii of the tangent granular-balls by referencing the best granular-ball. Algorithms are developed to generate the granular-balls with dynamic radius adjustments and to build the subsequent classifier. Finally, experimental results on nine UCI datasets demonstrate the effectiveness and efficiency of our approach.

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Data Availability and Access

The datasets used in this study are available in the UCI Machine Learning repository [http://archive.ics.uci.edu/ml].

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Acknowledgements

We would like to thank the Editor-in-Chief, Dr. Hamido Fujita, the anonymous reviewers, and Dr. Mengjun Hu for their critical and constructive comments. This work is supported by the National Natural Science Foundation of China (Nos. 62076040, 12471431), the Scientific Research Fund of Hunan Provincial Education Department (No. 22A0233), the Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing (No. 2018TP1018), the Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering (No. 2018MMAEZD10), and a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada.

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Jialong Pan contributed to Writing-Original draft preparation, Investigation and Experiments; Guangming Lang contributed to Methodology, Investigation, Writing-Review and Editing; Qimei Xiao contributed to Methodology and Writing-Review; Tian Yang contributed to Methodology and Writing-Review.

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Correspondence to Guangming Lang.

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Pan, J., Lang, G., Xiao, Q. et al. A framework of granular-ball generation for classification via granularity tuning. Appl Intell 55, 63 (2025). https://doi.org/10.1007/s10489-024-05904-1

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