An evolutionary approach to feature function generation in application to biomedical image patterns

P. Guo, P. Bhattacharya
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引用次数: 5

Abstract

A mechanism involving evolutionary genetic programming (GP) and the expectation maximization algorithm (EM) is proposed to generate feature functions, based on the primitive features, for an image pattern recognition system on the diagnosis of the disease OPMD. Experiments show that the propose algorithm achieves an average performance of 90.20% recognition rate on diagnosis, while reducing the number of feature dimensions from 11 primitive features to the space of a single generated feature.
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生物医学图像模式特征函数生成的进化方法
提出了一种结合进化遗传规划(GP)和期望最大化算法(EM)的机制,在原始特征的基础上生成OPMD图像模式识别系统的特征函数。实验表明,该算法在将11个原始特征的特征维数减少到单个生成特征的空间的同时,在诊断上实现了90.20%的平均识别率。
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Metaheuristics for graph bisection Bayesian network structure learning using cooperative coevolution Session details: Track 10: genetic programming Simulating human grandmasters: evolution and coevolution of evaluation functions An evolutionary approach to feature function generation in application to biomedical image patterns
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