Improved classification techniques by combining KNN and Random Forest with Naive Bayesian classifier

机译：通过将KNN和随机林与天真贝叶斯分类器组合来改进分类技术

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In Recent days, Information Technology walks into all spheres of life. The need for processing the information and analysing the processed information is one of the challenging task in any domain. Naive Bayes is one of the most elegant and simple classifier in data mining field. Irrespective of its feature independence assumptions, it surpasses all other classification techniques by yielding very good performance. In this paper, we attempted to increase the prediction accuracy of Naive Bayes model by integrating it with K nearest neighbours (KNN) and Random forest (RF). We believe that the simplicity of this approach and its great performance will be helpful for any classification.

机译：最近几天，信息技术走进所有生命领域。处理信息和分析处理信息的需求是任何域中的具有挑战性的任务之一。天真的贝父是数据矿业领域最优雅而简单的分类器之一。无论其特征独立假设如何，它通过产生非常好的性能来超越所有其他分类技术。在本文中，我们试图通过将其与K最近邻居（KNN）和随机林（RF）集成来提高天真贝叶斯模型的预测准确性。我们相信这种方法的简单性及其良好性能将有助于任何分类。

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《International Conference on Engineering Technology》|2015年||共4页
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作者
Devi R.Gayathri; Sumanjani P.;
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正文语种
中图分类计算技术、计算机技术;
关键词
KNN; Na?ve Bayesian Classifier; Random Forest;

机译：knn;na？ve贝叶斯分类器;随机森林;

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1. An improved naive Bayesian classifier technique coupled with anovel input solution method [rainfall prediction] [J] . Liu J.N.K., Li B.N.L., Dillon T.S. IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews . 2001,第2期

机译：改进的朴素贝叶斯分类器技术结合anovel输入解法[降雨预测]
2. An efficient classification framework for breast cancer using hyper parameter tuned Random Decision Forest Classifier and Bayesian Optimization [J] . Kumar Pratheep P., Bai Mary Amala V, Nair Geetha G. Biomedical signal processing and control . 2021,第Pta2期

机译：使用超参数调谐随机决策林类分类器和贝叶斯优化的高效分类框架
3. Exploratory study on classification of diabetes mellitus through a combined Random Forest Classifier [J] . Xuchun Wang, Mengmeng Zhai, Zeping Ren, BMC Medical Informatics and Decision Making . 2021,第1期

机译：通过组合随机林分类器糖尿病患者分类的探索性研究
4. Improved classification techniques by combining KNN and Random Forest with Naive Bayesian classifier [C] . Devi R.Gayathri, Sumanjani P. 2015 IEEE International Conference on Engineering amp; Technology . 2015

机译：通过将KNN和随机森林与朴素贝叶斯分类器结合使用的改进分类技术
5. Improving classification accuracy by using confidence measures to combine classifiers. [D] . Chadwick, Steve Patrick. 2000

机译：通过使用置信度度量组合分类器来提高分类准确性。
6. Exploratory study on classification of diabetes mellitus through a combined Random Forest Classifier [O] . Xuchun Wang, Mengmeng Zhai, Zeping Ren, 2021

机译：通过组合随机林分类器糖尿病患者分类的探索性研究
7. Table 1: Model comparison between naive Bayes, Bayesian network classifiers (BNC), and random forest. [O] . -1

机译：表1：朴素贝叶斯，贝叶斯网络分类器（BNC）和随机林之间的模型比较。

Improved classification techniques by combining KNN and Random Forest with Naive Bayesian classifier

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