Kuncheva L I
Department of Biomedical Cybernetics, Bulgarian Academy of Sciences, Sofia.
Comput Biol Med. 1990;20(6):421-31. doi: 10.1016/0010-4825(90)90023-i.
In this paper a fuzzy pattern recognition model is described, which is a tool to handle problems with noncrisp and multi-class membership of the objects. It is oriented to medical diagnostics, where the patients suffer from more than one disease in different degrees. Fuzzy pattern recognition is supposed to fit medical diagnostic problems better than conventional pattern recognition. The design of a multi-level fuzzy decision scheme is considered in order to derive high performance, taking into account expert logic and human experience. Two main topics are discussed--the criterion for evaluation of classification accuracy and the training rule. The implementation of fuzzy multi-level classifier is illustrated with real clinical data.
本文描述了一种模糊模式识别模型，它是一种处理对象具有不清晰和多类隶属度问题的工具。它面向医学诊断，在医学诊断中患者会不同程度地患有多种疾病。模糊模式识别被认为比传统模式识别更适合医学诊断问题。为了获得高性能，考虑专家逻辑和人类经验，研究了一种多级模糊决策方案的设计。讨论了两个主要主题——分类准确率的评估标准和训练规则。用实际临床数据说明了模糊多级分类器的实现。
