Numerous models have been proposed to reduce the classification error of Na¨ ve Bayes by weakening its attribute independence assumption and some have demonstrated remarkable error performance. Considering that ensemble learning is an effective method of reducing the classification error of the classifier, this paper proposes a double-layer Bayesian classifier ensembles (DLBCE) algorithm based on frequent itemsets. DLBCE constructs a double-layer Bayesian classifier (DLBC) for each frequent itemset the new instance contained and finally ensembles all the classifiers by assigning different weight to different classifier according to the conditional mutual information. The experimental results show that the proposed algorithm outperforms other outstanding algorithms.
本文利用形态学的方法确定聚类数目,并对单词-文档谱聚类方法进行改进.确定聚类数目主要分三个步骤:第一步将单词-文档谱聚类方法中产生的矩阵转换成可视化聚类趋势分析方法(visual assessment of tendency,VAT)灰度图,第二步利用灰度形态学、图像二值化、距离转换等图像处理技术过滤产生的VAT灰度图,第三步对过滤后的VAT灰度图建立信号图,并进行平滑处理,通过平滑后的信号图的波峰波谷数目确定文档集的聚类数目.实验表明,该方法能够提高单词-文档谱聚类方法的聚类效果.
