并行集成具有高可解释的TSK模糊分类器

针对分层Takagi-Sugeno-Kang (TSK)模糊分类器可解释性差,以及当增加或删除一个TSK模糊子分类器时Boosting模糊分类器需要重新训练所有TSK模糊子分类器等问题,提出一种并行集成具有高可解释的TSK模糊分类器EP-Q-TSK.该集成模糊分类器每个TSK模糊子分类器可以使用最小学习机(LLM)被并行地快速构建.作为一种新的集成学习方式,该分类器利用每个TSK模糊子分类器的增量输出来扩展原始验证数据空间,然后采用经典的模糊聚类算法FCM获取一系列代表性中心点,最后利用KNN对测试数据进行分类.在标准UCI数据集上,分别从分类性能和可解释性两方面验证了EP-Q-TSK的有效性.

Ensemble TSK fuzzy classifiers with parallel learning and high interpretability

Traditional ensemble Takagi-Sugeno-Kang(TSK) fuzzy subclassifiers face such challenges, hierarchical learning have no interpretability, because of the presence of intermediate variables, and when a new TSK fuzzy subclassifier is added to or removed from the structure of the current fuzzy classifier, boosting learning must retrain each TSK fuzzy subclassifier by appropriately assigning new weights. Therefore, An ensemble framework EP-Q-TSK of TSK fuzzy subclassifiers with parallel learning way is proposed. The proposed framework has the following distinctive characteristics: 1) Each TSK fuzzy subclassifier can be built quickly with least learning machine (LLM) in parallel; 2) As a novel ensemble learning, the proposed framework augments the original validation data space with the outputs of each TSK subclassifier in an incremental and inexpensive way, and then speed up the final classification on the validation data by using the FCM and the KNN method; 3) Enhanced classification performance by FCM & KNN is experimentally revealed, and the experimental results on benchmark datasets indicate the effectiveness of EP-Q-TSK and its parallel learning method in the sense of both enhanced classification performance and interpretability.