结合最优类别信息离散的细粒度超网络微阵列数据分类

摘要/Abstract

摘要：

针对传统演化超网络只能有效处理二值数据输入的问题，提出一种结合最优类别信息离散（Optimal Class-Dependent Discretization， OCDD)的细粒度演化超网络模型，对连续数据进行离散化生成细粒度二进制编码，并通过对其进行演化学习得到具备决策能力的超网络分类器.该方法避免了传统超网络模型对连续数据进行直接二值化后的高信息损失，使演化超网络的概率估计更接近于数据真实分布，提高了超网络的决策分类能力.对结肠癌、肺癌、前列腺癌和急性白血病4种DNA微阵列数据集进行实验的结果表明，结合OCDD的细粒度演化超网络具有比传统演化超网络更高的识别率和鲁棒性.

关键词: 模式识别, 机器学习, 超网络, 细粒度

Abstract:

To overcome the disadvantages in the traditional hypernetwork, an optimal class-dependent discretization (OCDD)based fine-grain evolutionary hypernetwork model was proposed, in which a continuous data was discretized as a fine-grain data and was used to evolve a hypernetwork classifier. With OCDD, the proposed fine-grain evolutionary hypernetwork lose less information than the traditional hypernetwork which directly convert a continuous data to a binary data. Moreover, fine-grain discrete data processing within the proposed hypernetwork model improves the quality of the probability estimation of a hypernetwork to the training data sets, and further enhances the performance of the hypernetwork classifier. The experimental results on colon, lung, prostate and Leukemia DNA microarray data sets show that the proposed method has a higher recognition rate and better robustness than the traditional evolutionary hypernetworks.

Key words: pattern recognition, machine learning, hypernetworks, fine-grain

中图分类号:

TP 391.4

王进，张军，胡白帆. 结合最优类别信息离散的细粒度超网络微阵列数据分类[J]. 上海交通大学学报（自然版）, 2013, 47(12): 1856-1862.

WANG Jin,ZHANG Jun,HU Baifan . Optimal Class-Dependent Discretization-Based Fine-Grain Hypernetworks for Classification of Microarray Data[J]. Journal of Shanghai Jiaotong University, 2013, 47(12): 1856-1862.

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