基于属性组合的集成学习算法

针对样本由数字属性构成的分类问题，在AdaBoost算法流程基础上，改传统的基于单属性分类器构造方法为基于组合属性分类器构造方法，提出了一种基于样本属性线性组合的集成学习算法。对属性组合系数的构造，提出了一般性的构造思路，按照该思路，提出了几种具体的组合系数构造方法，并对构造方法的科学合理性进行了分析。利用UCI机器学习数据集中的数据对提出的方法进行了实验与分析，结果表明，基于属性组合的集成学习算法不仅有是有效的，而且比传统AdaBoost算法好

Ensemble learning algorithm on attribute combination

Concerning the classification of samples being composed of digital attributes,an ensemble learning algorithm based on linear combination of samples attributes was proposed.It constructed classifiers based on combined attributes instead of single attribute by traditional AdaBoost algorithm.The general construction idea for attribute combination coefficients was put forward.In accordance with the idea,several concrete construction methods for combination coefficients were given and analyzed to be scientific a...