Jain R, Mazumdar J, Moran W
Department of Applied Mathematics, University of Adelaide, South Australia.
Australas Phys Eng Sci Med. 1998 Sep;21(3):141-7.
This paper presents an application of a genetic-algorithm-based representation of fuzzy rules for the classification of coronary artery disease data and breast cancer data. The performance of this fuzzy classifier for classification of coronary artery disease and breast cancer data is evaluated. In this study the concept of fuzzy if-then has been applied of rules proposed by Ishibuchi et al. for a multi dimensional data classification problem which leads to higher classification power. The fitness value of each fuzzy if-then rule was determined by the numbers of correctly and wrongly classified training patterns for that rule. The classification power on real world data for coronary artery disease and breast cancer was thus demonstrated by computer simulations.
本文介绍了一种基于遗传算法的模糊规则表示在冠状动脉疾病数据和乳腺癌数据分类中的应用。评估了这种模糊分类器对冠状动脉疾病和乳腺癌数据进行分类的性能。在本研究中，模糊“如果-那么”概念已应用于石部等人提出的用于多维数据分类问题的规则，这导致了更高的分类能力。每个模糊“如果-那么”规则的适应度值由该规则正确分类和错误分类的训练模式数量确定。通过计算机模拟证明了对冠状动脉疾病和乳腺癌的真实世界数据的分类能力。
