论文标题
具有混合属性数据的神经模糊分类器的在线学习算法
An Online Learning Algorithm for a Neuro-Fuzzy Classifier with Mixed-Attribute Data
论文作者
论文摘要
一般模糊的最低 - 最大神经网络（GFMMNN）是用于数据分类的有效神经模糊系统之一。但是，其原始学习算法的缺点之一是无法处理和从混合属性数据中学习。虽然可以将编码方法的分类特征与GFMMNN学习算法一起使用，但它们表现出许多缺点。文献中提出的其他方法不适合在线学习，因为它们需要在学习阶段可用的整个培训数据。随着许多应用领域中流数据的音量和速度的快速变化，越来越需要构造的模型可以学习并适应实时的连续数据变化，而无需其完整的重新培训或访问历史数据。本文为GFMMNN提出了一种扩展的在线学习算法。所提出的方法可以处理具有连续和分类特征的数据集。与GFMM模型的其他相关学习算法相比，广泛的实验证实了所提出方法的出色和稳定分类性能。
General fuzzy min-max neural network (GFMMNN) is one of the efficient neuro-fuzzy systems for data classification. However, one of the downsides of its original learning algorithms is the inability to handle and learn from the mixed-attribute data. While categorical features encoding methods can be used with the GFMMNN learning algorithms, they exhibit a lot of shortcomings. Other approaches proposed in the literature are not suitable for on-line learning as they require entire training data available in the learning phase. With the rapid change in the volume and velocity of streaming data in many application areas, it is increasingly required that the constructed models can learn and adapt to the continuous data changes in real-time without the need for their full retraining or access to the historical data. This paper proposes an extended online learning algorithm for the GFMMNN. The proposed method can handle the datasets with both continuous and categorical features. The extensive experiments confirmed superior and stable classification performance of the proposed approach in comparison to other relevant learning algorithms for the GFMM model.
