R-FCCL:一种面向高维数据的稳健模糊概念认知学习方法  

R-FCCL:An Approach of Fuzzy-Based Concept-Cognitive Learning with Robustness for High-Dimensional Data

作　　者：郭豆豆 徐伟华 Guo Doudou;Xu Weihua(College of Artificial Intelligence,Southwest University,Chongqing 400715)

机构地区：[1]西南大学人工智能学院,重庆400715

出　　处：《计算机研究与发展》2025年第2期383-396,共14页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(62376229);重庆市自然科学基金(CSTB2023NSCQ-LZX0027)。

摘　　要：随着全球信息化的高速发展,高维数据挖掘与知识发现成为了人工智能领域亟待破解的科学问题之一.然而,由于高维数据中样本的稀疏性与特征的冗余性,传统统计学模型和机器学习方法的泛化性和可解释性遇到极大的挑战.为此,针对高维数据与知识弱演化能力之间不平衡的科学问题,利用三支概念求解复杂问题的认知机理,提出了一种新的概念建模方法,即稳健模糊概念认知学习(fuzzy-based concept-cognitive learning with robustness,R-FCCL).首先,借助概念的最大相似性原则,建立了基于RFCCL的高维数据分类系统,并从概念的角度出发,研究了高维数据的知识结构和认知学习机理.进一步,利用模糊三支概念的正、负算子从2个不同的角度刻画了模糊环境概念认知学习过程,进而基于概念融合的模糊三支概念完成概念辨识和数据分类.通过在12个真实数据集与12种分类方法的实验分析,验证所提方法具有较好的鲁棒性和有效性.With the rapid development of global informationization,data mining and knowledge discovery of highdimensional data have been a hotspot in the field of artificial intelligence and data science.However,the sparse sample and redundant feature issues of high-dimensional data make it challenging to ensure the generalization and interpretability of traditional statistical models and machine learning methods.Hence,we present fuzzy-based conceptcognitive learning with robustness for the imbalance problem between high-dimensional data and weak knowledge evolution ability.The main idea is to explore the knowledge structure and cognitive learning mechanism of highdimensional data from the concept perspective.We propose a high-dimensional data classification method based on the concept-cognitive learning mechanism in the fuzzy formal context.Furthermore,the cognitive learning process of fuzzy concepts is described from two different perspectives by the positive and negative cognitive learning operators of fuzzy three-way concepts.Finally,the fusion of fuzzy three-way concepts completes the task of concept identification and data classification.Extensive experiments performed on 12 real data sets compared with 12 state-of-the-art classification methods also verify the robustness and effectiveness of the proposed method.The considered framework can provide a convenient novel tool for high-dimensional data knowledge discovery research and fuzzy-based conceptcognitive learning.

关 键 词：形式概念分析 概念认知学习 粒计算 高维数据 三支决策 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]