基于快速超粒方生成算法的分类器模型

A classifier model based on the fast granular hypercube generation algorithm

摘要

在空间划分时粒球计算方法存在半径敏感性、覆盖盲区与区域重叠缺陷等问题,本文提出基于n维超长方体的信息粒化方法。突破传统球形结构约束,采用n维超长方体几何模型,建立无盲区、无重叠的空间划分理论体系,提出快速超粒方生成(fast granular hypercube generation, FG HG)算法,通过维度自适应分割机制实现高效空间划分,与传统粒球生成算法相比,FGHG算法在计算效率方面具有显著优势,设计快速超粒方分类器(fast granular hypercube classifier, FGHC)。为验证所提算法的有效性,选取13个真实数据集评估,FGHC算法的分类精度和F1分数均较高。本文建立的超粒方计算范式,为解决复杂数据空间划分问题提供新的理论框架。

Abstract

To address the problem of radius sensitivity in granular ball based spatial partitioning, which results in coverage gaps or region overlaps, an information granulation method is proposed based on n-dimensional hypercubes. The traditional constraint of spherical structures is overcome by introducing a novel n-dimensional hypercube geometric model, which establishes a theoretical framework for spatial partitioning without coverage gaps or overlapping regions. A fast granular hypercube generation (FGHG) algorithm is proposed, which utilizes a dimension-adaptive partitioning mechanism to enable efficient spatial division. Compared with traditional granular ball generation algorithms, FGHG algorithm demonstrates significant advantages in computational efficiency. A fast granular hypercube classifier (FGHC) is designed. To validate the effectiveness of the proposed algorithm, a systematic evaluation is conducted on 13 real-world datasets from the repository, where FGHG algorithm achieving improvements in both classification accuracy and F1 score. The granular hypercube computing paradigm established in this study provides a novel theoretical framework for tackling complex spatial partitioning problems in data analysis.

关键词

粒计算 / 超粒方 / 信息粒化 / 分类器 / 粒球计算

Key words

granular computing / granular hypercube / information granulation / classifier / granular ball computing

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基金资助

国家自然科学基金资助项目(12061067)

国家自然科学基金资助项目(62176033)

重庆市自然科学基金面上资助项目(CSTB2023NSCQ-MSX0707)