Khalilpour Darzi, Mohammad Rasoul and Khedmati, Majid and Niaki, Seyed Taghi Akhavan (2021) Correlation-augmented Naïve Bayes (CAN) Algorithm: A Novel Bayesian Method Adjusted for Direct Marketing. Applied Artificial Intelligence, 35 (15). pp. 2013-2036. ISSN 0883-9514
![[thumbnail of Correlation augmented Na ve Bayes CAN Algorithm A Novel Bayesian Method Adjusted for Direct Marketing.pdf]](http://ebooks.pubstmlibrary.com/style/images/fileicons/text.png)
Correlation augmented Na ve Bayes CAN Algorithm A Novel Bayesian Method Adjusted for Direct Marketing.pdf - Published Version
Download (4MB)
Abstract
Direct marketing identifies customers who buy, more probable, a specific product to reduce the cost and increase the response rate of a marketing campaign. The advancement of technology in the current era makes the data collection process easy. Hence, a large number of customer data can be stored in companies where they can be employed to solve the direct marketing problem. In this paper, a novel Bayesian method titled correlation-augment naïve Bayes (CAN) is proposed to improve the conventional naïve Bayes (NB) classifier. The performance of the proposed method in terms of the response rate is evaluated and compared to several well-known Bayesian networks and other well-known classifiers based on seven real-world datasets from different areas with different characteristics. The experimental results show that the proposed CAN method has a much better performance compared to the other investigated methods for direct marketing in almost all cases.
Item Type: | Article |
---|---|
Subjects: | ArticleGate > Computer Science |
Depositing User: | Managing Editor |
Date Deposited: | 16 Jun 2023 04:10 |
Last Modified: | 18 May 2024 09:06 |
URI: | http://ebooks.pubstmlibrary.com/id/eprint/3027 |