2017
DOI: 10.1002/isaf.1403
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A two‐step system for direct bank telemarketing outcome classification

Abstract: Summary A two‐step system is presented to improve prediction of telemarketing outcomes and to help the marketing management team effectively manage customer relationships in the banking industry. In the first step, several neural networks are trained with different categories of information to make initial predictions. In the second step, all initial predictions are combined by a single neural network to make a final prediction. Particle swarm optimization is employed to optimize the initial weights of each ne… Show more

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Cited by 20 publications
(12 citation statements)
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“…Moreover, a profit driven artificial NN approach was proposed in [62] and a similar study applied two steps model of K-mean clustering and classification [63]. Recently, Lahmiri [64] proposed a two step system that combined a NN ensemble model and Particle Swarm Optimization for optimizing the initial weights of each NN in the ensemble framework. This was also verified by the bank direct marketing data with outstanding performance in relation to the baseline approaches.…”
Section: Customer Development and Customizationmentioning
confidence: 99%
“…Moreover, a profit driven artificial NN approach was proposed in [62] and a similar study applied two steps model of K-mean clustering and classification [63]. Recently, Lahmiri [64] proposed a two step system that combined a NN ensemble model and Particle Swarm Optimization for optimizing the initial weights of each NN in the ensemble framework. This was also verified by the bank direct marketing data with outstanding performance in relation to the baseline approaches.…”
Section: Customer Development and Customizationmentioning
confidence: 99%
“…They used a regression model to process and select important features that affect business failure prediction and then, a probabilistic neural network model was adopted to achieve high probability of success for data classification. To reduce the risk of noise in learning processes of algorithms, Lahmiri () proposed a two – step prediction approach with an application on bank telemarketing problem. The study shows that two – step system is robust to nonlinear and noisy data and it is suitable to make fast and easy prediction for large datasets.…”
Section: Proposed Technique: Data Mining Based Outlier Detectionmentioning
confidence: 99%
“…The purpose of the current work is to study the effectiveness of various ensemble learning and classification systems in the context of financial data classification; especially in corporate bankruptcy prediction and credit scoring which have previously received a large attention (Abdou & Poiton, ; Çelik, ; Davalos, Leng, Feroz, & Cao, ; Figini, Savona, & Vezzoli, ; Lahmiri, ; Lahmiri, ; Lahmiri & Bekiros, ; Lahmiri & Gagnon, ; Mendes, Cardoso, Mário, Martinez, & Ferreira, ; Peat & Jones, ; Pendharkar, ; Quek, Zhou, & Lee, ; Savona & Vezzoli, ; Sun, ; Trinkle & Baldwin, ).…”
Section: Introductionmentioning
confidence: 99%