Evolving Single- And Multi-Model Fuzzy Classifiers with FLEXFIS-Class
Evolving Single- And Multi-Model Fuzzy Classifiers with FLEXFIS-Class
复制标题
使用 FLEXFIS-Class 改进单模型和多模型模糊分类器
DOI：
10.1109/fuzzy.2007.4295393
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Xiaowei Zhou
中科院分区：
文献类型：
--
作者：
E. Lughofer;P. Angelov;Xiaowei Zhou
本文提出了一种增量式自适应训练单模型和多模型模糊分类器的新方法--FLEXFIS-Class。单模型的情况下，利用传统的零阶模糊分类模型架构的高斯模糊集的规则的前件，清晰的类标签的规则的后果和规则的权重代表的置信度值的类标签。在多模型的情况下，FLEXFIS-Class利用指标矩阵回归的思想，为每个单独的类发展一个Takagi-Sugeno模糊模型，并将单个模型的预测组合成最终的分类声明。本文包括一种技术，用于提高预测质量，每当数据流中发生漂移。基于在线自适应图像分类框架进行实证分析，其中显示生产项目的图像应被分类为好或坏。这种分析将包括不断发展的单模型和多模型模糊分类器与传统的批量建模方法在新的在线数据上实现的预测精度的比较。它也将被证明，多模型架构可以优于传统的单模型架构（“经典”模糊分类模型）的预测精度方面的所有数据集。
In this paper a new method for training single-model and multi-model fuzzy classifiers incrementally and adaptively is proposed, which is called FLEXFIS-Class. The evolving scheme for the single-model case exploits a conventional zero-order fuzzy classification model architecture with Gaussian fuzzy sets in the rules antecedents, crisp class labels in the rule consequents and rule weights standing for confidence values in the class labels. In the multi-model case FLEXFIS-Class exploits the idea of regression by an indicator matrix to evolve a Takagi-Sugeno fuzzy model for each separate class and combines the single models' predictions to a final classification statement. The paper includes a technique for increasing the prediction quality, whenever a drift in a data stream occurs. An empirical analysis will be given based on an online, adaptive image classification framework, where images showing production items should be classified into good or bad ones. This analysis will include the comparison of evolving single-and multi-model fuzzy classifiers with conventional batch modelling approaches with respect to achieved prediction accuracy on new online data. It will also be shown that multi-model architecture can outperform conventional single-model architecture ('classical' fuzzy classification models) for all data sets with respect to prediction accuracy.