基于余弦相似度和实例加权改进的贝叶斯算法

面对大量样本特征时很多分类器无法取得较好的分类效果，样本数有限导致贝叶斯算法无法获得精确的联合概率分布估计，在样本局部构建高质量分类器需要有效的样本相似性度量指标. 针对以上问题，提出了一种基于余弦相似度进行实例加权改进的朴素贝叶斯分类算法. 算法考虑特征对分类的决策权重不同，使用余弦相似度度量样本的相似性，选出最优训练样本子集，用相似度值作为训练样本的权值来训练修正后的贝叶斯模型进行分类. 基于UCI数据集的对比实验结果表明，提出的改进算法易于实现且具有更高的平均分类准确率.

Improved Naïve Bayes Algorithm Based on Weighted Instance with Cosine Similarity

Many classifiers cannot get good results facing numerous sample features, bayes algorithms get poor estimate of joint probability distribution with limited samples, effective similarity measure is needed to build a local classifier. Considering these problems, an improved multinomial Naïve Bayes algorithm based on weighted instances (IWIMNB) with cosine similarity is proposed. Taking different attributes contributing differently to the classification decision weight into account this algorithm uses cosine similarity as a metric of the similarity between training and validation instances. Selected training instances weighted by cosine similarity are used to train modified Naïve Bayes model. Results of final experiments show that the average classification accuracy of the proposed IWIMNB algorithm gets significant improvement.