线性判别分析
模式识别(心理学)
核Fisher判别分析
费希尔核
人工智能
特征向量
计分算法
特征提取
线性分类器
数学
分类器(UML)
最优判别分析
支持向量机
特征(语言学)
感知器
计算机科学
遗传程序设计
判别式
不相交集
主成分分析
人工神经网络
面部识别系统
组合数学
哲学
语言学
作者
Hong Guo,Asoke K. Nandi
标识
DOI:10.1016/j.patcog.2005.10.001
摘要
This paper proposes a novel method for breast cancer diagnosis using the feature generated by genetic programming (GP). We developed a new feature extraction measure (modified Fisher linear discriminant analysis (MFLDA)) to overcome the limitation of Fisher criterion. GP as an evolutionary mechanism provides a training structure to generate features. A modified Fisher criterion is developed to help GP optimize features that allow pattern vectors belonging to different categories to distribute compactly and disjoint regions. First, the MFLDA is experimentally compared with some classical feature extraction methods (principal component analysis, Fisher linear discriminant analysis, alternative Fisher linear discriminant analysis). Second, the feature generated by GP based on the modified Fisher criterion is compared with the features generated by GP using Fisher criterion and an alternative Fisher criterion in terms of the classification performance. The classification is carried out by a simple classifier (minimum distance classifier). Finally, the same feature generated by GP is compared with a original feature set as the inputs to multi-layer perceptrons and support vector machine. Results demonstrate the capability of this method to transform information from high-dimensional feature space into one-dimensional space and automatically discover the relationship among data, to improve classification accuracy.
科研通智能强力驱动
Strongly Powered by AbleSci AI