提出一种适用于动态环境的多分类在线模糊分类器。
A Study on Multi-Class Online Fuzzy Classifiers for Dynamic Environments
- 基于在线学习机制，逐步接收数据并更新规则。
- 在合成数据与多个基准数据集上验证了性能优势。
- 适合需要实时适应变化的多分类场景。
本文提出一种适用于动态环境的多分类在线模糊分类器。模糊分类器由一组模糊条件-结论规则构成，其中前提部分的模糊集合由人工预先设定，而结论部分的实数值通过训练数据学习得到。在在线框架下，训练数据并非一次性全部可用，而是分时步逐步到达：每个时间步仅能获取少量样本，后续样本在后续时间步才可获得。传统在线模糊分类器仅针对二分类问题。本文研究将其扩展至多分类问题。通过在合成动态数据及多个基准数据集上的数值实验，评估了该多分类在线模糊分类器的性能。
原文摘要 · Abstract (English)
This paper proposes a multi-class online fuzzy classifier for dynamic environments. A fuzzy classifier comprises a set of fuzzy if-then rules where human users determine the antecedent fuzzy sets beforehand. In contrast, the consequent real values are determined by learning from training data. In an online framework, not all training dataset patterns are available beforehand. Instead, only a few patterns are available at a time step, and the subsequent patterns become available at the following time steps. The conventional online fuzzy classifier considered only two-class problems. This paper investigates the extension to the conventional fuzzy classifiers for multi-class problems. We evaluate the performance of the multi-class online fuzzy classifiers through numerical experiments on synthetic dynamic data and also several benchmark datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。