Optimization of architectural art teaching model based on Naive Bayesian classification algorithm and fuzzy model,Journal of Intelligent & Fuzzy Systems

当前位置： X-MOL 学术 › J. Intell. Fuzzy Syst. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Optimization of architectural art teaching model based on Naive Bayesian classification algorithm and fuzzy model
Journal of Intelligent & Fuzzy Systems ( IF 1.7 ) Pub Date : 2020-07-03 , DOI: 10.3233/jifs-179966
Ying Liu _{1,

2}

Affiliation

At present, the teaching of architectural art in China is still relatively traditional, and there are still some problems in the actual teaching. Based on this, this study combines the Naive Bayesian classification algorithm with the fuzzy model to construct a new architectural art teaching model.In teaching, the Naive Bayesian classification algorithm generates only a small number of features for each item in the training set, and it only uses the probability calculated in the mathematical operation to train and classify the item. Moreover, by combining the fuzzy model, the materials needed for architectural art teaching can be quickly generated, and the teaching principles and implementation strategies of architectural art are summarized. In addition, this paper proposes an attribute weighted classification algorithm combining differential evolution algorithm with Naive Bayes. The algorithm assigns weights to each attribute based on the Naive Bayesian classification algorithm and uses differential evolution algorithm to optimize the weights. The research shows that the method proposed in this paper has certain effect on the optimization of architectural art teaching mode.

更新日期：2020-07-03

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11