| 摘要: |
| 遗忘学习在隐私保护、减少污染数据影响和冗余数据处理等方面具有重要应用价值, 但现有的遗忘学习方法多用于神经网络等黑箱模型中, 在可解释的TSK模糊分类系统中实现高效的单类和多类遗忘仍面临挑战. 为此, 提出了一种面向分类的TSK模糊遗忘学习方法(TSK-FUC). 首先, 通过各规则的前件参数在(单类或多类)遗忘数据上的归一化激活强度, 将规则库划分为与遗忘数据高相关的删减规则集、与遗忘数据低相关的保留规则集以及与遗忘数据和保留数据关系较为重叠的更新规则集. 继而采取差异化处理策略: 直接剔除删减规则集, 以消除主要信息残留, 并降低分类系统参数量; 完整保存保留规则集, 以缩小遗忘学习过程的参数调整范围; 对于更新规则集, 通过为每个遗忘类添加噪声, 用以进一步消除规则中关于遗忘数据的信息, 从而实现单类和多类遗忘. 实验结果表明, 在16个真实数据集的已建好的0阶和1阶TSK分类系统上, TSK-FUC能够较为准确地划分规则空间, 并结合差异化的处理展现出良好的单类和多类遗忘效果. 该方法在保持规则库可解释性的同时, 使得遗忘学习后的TSK模糊分类系统在结构上更加轻量化. |
| 关键词: 遗忘学习 模糊遗忘 Takagi-Sugeno-Kang (TSK)模糊系统 规则分类 轻量化 |
| DOI：10.13328/j.cnki.jos.007503 |
| 分类号:TP18 |
| 基金项目:国家重点研发计划(2022YFE0112400) |
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| TSK Fuzzy Unlearning Method for Classification |
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WANG Pan, WANG Shi-Tong
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School of Artificial Intelligence and Computer Science (School of Software), Jiangnan University, Wuxi 214122, China
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| Abstract: |
| Unlearning has significant application value in safeguarding privacy, mitigating the impact of contaminated samples, and processing redundant data. However, existing unlearning methods are mostly applied to black-box models such as neural networks, while achieving efficient single-class and multi-class unlearning in interpretable TSK fuzzy classification systems remains challenging. To address this, this study proposes a TSK fuzzy unlearning method for classification (TSK-FUC). First, the rule base is divided into three subsets using the normalized activation strengths of rule antecedent parameters on the (single/multi-class) forgotten data: 1) a deleted rule set that is highly relevant to the forgotten data, 2) a retained rule set with low relevance to the forgotten data, and 3) an updated rule set showing overlapping relevance to both the retained and forgotten data. Subsequently, differential processing strategies are applied: the deleted rule set is directly removed to eliminate major information residues and reduce the number of system parameters; the retained rule set is fully preserved to reduce parameter adjustment scope during unlearning; and for the updated rules, class-specific noise is added to the consequent parameters to further eliminate information related to the forgotten data, thus achieving single-class and multi-class unlearning. Experimental results on 16 benchmark datasets demonstrate that TSK-FUC accurately partitions the rule space and exhibits effective single-class and multi-class unlearning performance through differentiated processing in both 0-order and 1-order established TSK fuzzy classification systems. This method maintains the interpretability of the rule base while rendering the TSK fuzzy classification system more lightweight in terms of structure after unlearning. |
| Key words: unlearning fuzzy unlearning Takagi-Sugeno-Kang (TSK) fuzzy system rule classification lightweight |
