Data mining is one of the most effective statistical methodologies to investigate a variety of problems in areas including pattern recognition, machine learning, bioinformatics, chemometrics, and statistics. In particular, statistically-sophisticated procedures that emphasize on reliability of results and computational efficiency are required for the analysis of high-dimensional data. Optimization principles can play a significant role in the rationalization and validation of specialized data mining procedures. This paper presents a novel methodology which is Multi-Choice Wavelet Thresholding (MCWT) based three-step methodology consists of three processes: perception (dimension reduction), decision (feature ranking), and cognition (model selection). In these steps three concepts known as wavelet thresholding, support vector machines for classification and information complexity are integrated to evaluate learning models. Three published data sets are used to illustrate the proposed methodology. Additionally, performance comparisons with recent and widely applied methods are shown.

中文翻译：

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基于多选择小波阈值的二进制分类方法

数据挖掘是研究模式识别，机器学习，生物信息学，化学计量学和统计领域中各种问题的最有效的统计方法之一。特别是，对于高维数据的分析，需要强调结果可靠性和计算效率的统计上复杂的过程。优化原则可以在专用数据挖掘程序的合理化和验证中发挥重要作用。本文提出了一种新颖的方法，它是基于多选择小波阈值（MCWT）的三步方法，它由三个过程组成：感知（降维），决策（特征排名）和认知（模型选择）。在这些步骤中，三个概念称为小波阈值处理 用于分类和信息复杂性的支持向量机被集成以评估学习模型。使用三个已发布的数据集来说明建议的方法。此外，还显示了与最近和广泛应用的方法的性能比较。