改进的贝叶斯算法在商品分类中的应用研究  

Research on Application of Improved Bayesian Algorithm in Commodity Classification

作　　者：邵欣欣[1] SHAO Xinxin(Department of Software Engineering,Dalian Neusoft University of Information,Dalian 116023,China)

机构地区：[1]大连东软信息学院软件工程系,辽宁大连116023

出　　处：《软件工程》2021年第7期28-30,27,共4页Software Engineering

基　　金：辽宁省自然科学基金(2019-ZD-0354).

摘　　要：针对采用贝叶斯分类器算法进行商品描述分类时,出现大量混淆性词汇从而无法保证特征间独立的问题,提出了采用停用词优化的贝叶斯分类器算法,通过词频统计和词性筛选的方式,过滤掉大部分混淆性词汇,从而保证特征独立。针对相似类别无法准确区分的问题,提出了子模型训练的解决方案,对易混淆类别单独进行训练并记录训练过程,在测试阶段根据结果判断并使用子模型,从而实现细化区分。实验表明,优化方案确实可行,可以获得97.80%的准确率。A large number of confusing words appear when Naive Bayes classifier is used to classify commodity description.This makes it difficult to ensure independence between features.This paper proposes a Bayesian classifier algorithm optimized by stop words.Most confusing words can be filtered out by using word frequency statistics word part-of-speech screening.Aiming at the problem that similar categories cannot be distinguished,confusing categories are trained separately and the training process is recorded.In test phase,sub-models are judged and then used according to the results,so to realize detailed distinction.Experiments show that the optimized solution is indeed feasible,and can reach an accuracy of 97.80%.

关 键 词：朴素贝叶斯分类器 停用词 子模型训练 商品分类 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]