不完整数据集的信息熵集成分类算法

摘要

Ensemble method is a simple and effective method to deal with incomplete data for classification. However, the weight of each sub-classifier in ensemble classification algorithm for incomplete data is mainly determined by the size and dimension of corresponding sub-dataset at present. The contributions of the missing attributes are different, and information entropy is introduced to measure these differences, thus, a novel algorithm for incomplete data named Entropy Ensemble Classification Algorithm (EECA) is proposed in this paper. The ensemble classifier with BP neural network being base classifier is applied on UCI dataset. The experimental results show that EECA determining the weight for sub-classifier by information entropy is better than the algorithm by using simple weight.%集成方法是处理包含缺失属性数据集分类问题的一种简单有效的方法，但目前针对不完整数据的集成分类算法在衡量各子分类器的权重时只考虑对应的数据子集的维数和大小。考虑到不完整数据集的缺失属性对类别的贡献度，使用信息熵衡量缺失属性之间的差异，提出一种新的针对不完整数据的集成学习分类算法---信息熵集成分类算法( EECA)。应用以BP神经网络为基础分类器的集成分类器在UCI数据集上进行实验。实验结果表明， EECA比简单使用缺失属性的多少计算子分类器权重的方法更有效，最终结果准确度更高。