Correlation-augmented Naïve Bayes (CAN) Algorithm: A Novel Bayesian Method Adjusted for Direct Marketing
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...
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| Published in | Applied artificial intelligence Vol. 35; no. 15; pp. 2013 - 2036 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Philadelphia
Taylor & Francis
15.12.2021
Taylor & Francis Ltd Taylor & Francis Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0883-9514 1087-6545 1087-6545 |
| DOI | 10.1080/08839514.2021.1997226 |
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| 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. |
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| AbstractList | 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. |
| Author | Khedmati, Majid Khalilpour Darzi, Mohammad Rasoul Niaki, Seyed Taghi Akhavan |
| Author_xml | – sequence: 1 givenname: Mohammad Rasoul surname: Khalilpour Darzi fullname: Khalilpour Darzi, Mohammad Rasoul organization: Sharif University of Technology – sequence: 2 givenname: Majid orcidid: 0000-0001-8803-0658 surname: Khedmati fullname: Khedmati, Majid email: Khedmati@Sharif.edu organization: Sharif University of Technology – sequence: 3 givenname: Seyed Taghi Akhavan orcidid: 0000-0001-6281-055X surname: Niaki fullname: Niaki, Seyed Taghi Akhavan organization: Sharif University of Technology |
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| Title | Correlation-augmented Naïve Bayes (CAN) Algorithm: A Novel Bayesian Method Adjusted for Direct Marketing |
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