基于邻域覆盖约简的层次化规则学习算法  

Hierarchical Rule Learning Algorithm Based on Neighborhood Covering Reduction

作　　者：吴镒潾 刘浩阳 毛煜 林耀进 WU Yilin;LIU Haoyang;MAO Yu;LIN Yaojin(School of Computer Science,Minnan Normal University,Zhangzhou 363000,China;Key Laboratory of Data Science and Intelligence Application,Minnan Normal University,Zhangzhou 363000,China)

机构地区：[1]闽南师范大学计算机学院,福建漳州363000 [2]闽南师范大学数据科学与智能应用福建省高等学校重点实验室,福建漳州363000

出　　处：《安庆师范大学学报（自然科学版）》2023年第1期58-64,共7页Journal of Anqing Normal University(Natural Science Edition)

基　　金：国家自然科学基金(62076116);福建省自然科学基金(2021J2049)。

摘　　要：在大数据环境下的分类学习中,随着描述样本语义信息的丰富,数据的类别空间结构存在着层次化。然而,现有分层分类算法缺乏可解释性,为此本文提出了一种基于邻域覆盖约简的层次化规则学习算法。该算法框架主要包括:(1)定义了面向层次化结构数据的邻域覆盖约简模型;(2)定义了层次邻域系统中覆盖元的依赖度;(3)提出了一种基于覆盖元依赖度的层次化规则学习前向搜索算法。最后,实验表明本文所提算法的分类性能较优且具有较好的可解释性。In the classification learning under big data environment,there are hierarchical structures among categories in class space of data with rich semantic information.However,existing hierarchical classification algorithms lack interpretability.To address this problem,a hierarchical rule learning method based on neighborhood covering reduction is proposed.The framework consists of three parts:(1)a neighborhood covering reduction model for hierarchical structure data is defined;(2)a dependency degree of covering element in hierarchical neighborhood system is defined;(3)a forward hierarchical rule learning algorithm based on the dependency degree of covering element is designed.Finally,the effective and better interpretability of the proposed algorithm is verified by extensive experiments.

关 键 词：规则学习 邻域覆盖约简 层次分类 依赖度 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]