publication . Article . 2017

A divide-and-conquer strategy using feature relevance and expert knowledge for enhancing a data mining approach to bank telemarketing

Paulo Cortez; Paulo Rita; Paulo Rita; Sérgio Moro; Sérgio Moro;
Open Access English
  • Published: 26 Oct 2017
  • Publisher: Wiley
  • Country: Portugal
Abstract
The discovery of knowledge through data mining provides a valuable asset for addressing decision making problems. Although a list of features may characterize a problem, it is often the case that a subset of those features may influence more a certain group of events constituting a sub-problem within the original problem. We propose a divide-and-conquer strategy for data mining using both the data-based sensitivity analysis for extracting feature relevance and expert evaluation for splitting the problem of characterizing telemarketing contacts to sell bank deposits. As a result, the call direction (inbound/outbound) was considered the most suitable candidate fea...
Subjects
free text keywords: banking, data mining, divide and conquer, feature selection, marketing, Science & Technology, Control and Systems Engineering, Theoretical Computer Science, Computational Theory and Mathematics, Artificial Intelligence, Data science, Feature relevance, computer.software_genre, computer, Divide and conquer algorithms, Computer science
Funded by
FCT| UID/PSI/03125/2013
Project
UID/PSI/03125/2013
Center for Research and Social Intervention
  • Funder: Fundação para a Ciência e a Tecnologia, I.P. (FCT)
  • Project Code: 147229
  • Funding stream: 5876
51 references, page 1 of 4

Barraza, N. R., Moro, S., Ferreyra, M., & de la Peña, A. (2016). Information Theory based Feature Selection for Customer Classification. In Simposio Argentino de Inteligencia Artificial (ASAI 2016)- JAIIO 45 (Tres de Febrero, 2016).

Barzanti, L., & Giove, S. (2012). A decision support system for fund raising management based on the Choquet integral methodology. Expert Systems, 29(4), 359-373.

Bendre, M. R., & Thool, V. R. (2016). Analytics, challenges and applications in big data environment: a survey. Journal of Management Analytics, 3(3), 206-239. [OpenAIRE]

Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.

Chen, T., Xu, R., He, Y., & Wang, X. (2017). Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Systems with Applications, 72, 221-230. [OpenAIRE]

Cortez, P. (2010). Data mining with neural networks and support vector machines using the r/rminer tool. In Advances in Data Mining. Applications and Theoretical Aspects (pp. 572-583). Springer.

Cortez, P., & Embrechts, M. J. (2013). Using sensitivity analysis and visualization techniques to open black box data mining models. Information Sciences, 225, 1-17. [OpenAIRE]

Cortez, P., & Santos, M. F. (2015). Recent advances on knowledge discovery and business intelligence. Expert Systems, 32(3), 433-434.

Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87.

Duan, L., & Xu, L. D. (2012). Business intelligence for enterprise systems: a survey. IEEE Transactions on Industrial Informatics, 8(3), 679-687.

Fawcett, T. (2006). An introduction to roc analysis. Pattern recognition letters, 27, 861-874.

Fritsch, J., & Finke, M. (2012). Applying divide and conquer to large scale pattern recognition asks.

In Neural Networks: Tricks of the Trade (pp. 311-338). Springer.

Guyon, I., & Elissee, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3, 1157-1182.

Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., & Tibshirani, R. (2009). The elements of statistical learning volume 2. Springer.

51 references, page 1 of 4
Abstract
The discovery of knowledge through data mining provides a valuable asset for addressing decision making problems. Although a list of features may characterize a problem, it is often the case that a subset of those features may influence more a certain group of events constituting a sub-problem within the original problem. We propose a divide-and-conquer strategy for data mining using both the data-based sensitivity analysis for extracting feature relevance and expert evaluation for splitting the problem of characterizing telemarketing contacts to sell bank deposits. As a result, the call direction (inbound/outbound) was considered the most suitable candidate fea...
Subjects
free text keywords: banking, data mining, divide and conquer, feature selection, marketing, Science & Technology, Control and Systems Engineering, Theoretical Computer Science, Computational Theory and Mathematics, Artificial Intelligence, Data science, Feature relevance, computer.software_genre, computer, Divide and conquer algorithms, Computer science
Funded by
FCT| UID/PSI/03125/2013
Project
UID/PSI/03125/2013
Center for Research and Social Intervention
  • Funder: Fundação para a Ciência e a Tecnologia, I.P. (FCT)
  • Project Code: 147229
  • Funding stream: 5876
51 references, page 1 of 4

Barraza, N. R., Moro, S., Ferreyra, M., & de la Peña, A. (2016). Information Theory based Feature Selection for Customer Classification. In Simposio Argentino de Inteligencia Artificial (ASAI 2016)- JAIIO 45 (Tres de Febrero, 2016).

Barzanti, L., & Giove, S. (2012). A decision support system for fund raising management based on the Choquet integral methodology. Expert Systems, 29(4), 359-373.

Bendre, M. R., & Thool, V. R. (2016). Analytics, challenges and applications in big data environment: a survey. Journal of Management Analytics, 3(3), 206-239. [OpenAIRE]

Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.

Chen, T., Xu, R., He, Y., & Wang, X. (2017). Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Systems with Applications, 72, 221-230. [OpenAIRE]

Cortez, P. (2010). Data mining with neural networks and support vector machines using the r/rminer tool. In Advances in Data Mining. Applications and Theoretical Aspects (pp. 572-583). Springer.

Cortez, P., & Embrechts, M. J. (2013). Using sensitivity analysis and visualization techniques to open black box data mining models. Information Sciences, 225, 1-17. [OpenAIRE]

Cortez, P., & Santos, M. F. (2015). Recent advances on knowledge discovery and business intelligence. Expert Systems, 32(3), 433-434.

Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87.

Duan, L., & Xu, L. D. (2012). Business intelligence for enterprise systems: a survey. IEEE Transactions on Industrial Informatics, 8(3), 679-687.

Fawcett, T. (2006). An introduction to roc analysis. Pattern recognition letters, 27, 861-874.

Fritsch, J., & Finke, M. (2012). Applying divide and conquer to large scale pattern recognition asks.

In Neural Networks: Tricks of the Trade (pp. 311-338). Springer.

Guyon, I., & Elissee, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3, 1157-1182.

Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., & Tibshirani, R. (2009). The elements of statistical learning volume 2. Springer.

51 references, page 1 of 4
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