基于图像分割分类的混合数据挖掘方法

M. Panda, A. Hassanien, A. Abraham
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引用次数: 38

摘要

进化和谐搜索算法具有寻找局部和全局解空间的能力。相比之下,基于小波的特征选择,由于其能够提供信号函数的局部频率信息,使其成为一种有希望的有效分类方法。该方向的研究表明,基于小波的神经网络可能陷入局部最小值,而基于模糊和谐搜索的算法有效地解决了这一问题,并能够获得接近最优解。在此基础上,提出了基于小波的径向基函数RBF神经网络WRBF和基于特征子集和谐搜索的模糊区分分类器HSFD混合方法作为基于图像分割分类的数据挖掘技术。本文采用Lena RGB图像;用于分析的磁共振图像MR和计算机断层扫描CT图像。仿真结果表明,基于小波的RBF神经网络优于基于和谐搜索的模糊分类器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Data Mining Approach for Image Segmentation Based Classification
Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function RBF neural network WRBF and feature subset harmony search based fuzzy discernibility classifier HSFD approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image MR and Computed Tomography CT Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.
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