APPLYING A PRUNED REGRESSION TREE FOR FEATURE SELECTION ON CUSTOMER PURCHASING BEHAVIOR FORECASTING IN INSURANCE INDUSTRY

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SOROUSH ALIREZA; BAHREININEJAD ARDESHIR; GHASEM ESFAHANI REIHANEH

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Journal: IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH) Year:2011 | Volume:25 | Issue:4 (100) Page(s): 131-152

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Title

APPLYING A PRUNED REGRESSION TREE FOR FEATURE SELECTION ON CUSTOMER PURCHASING BEHAVIOR FORECASTING IN INSURANCE INDUSTRY

Author(s)

SOROUSH ALIREZA | BAHREININEJAD ARDESHIR | GHASEM ESFAHANI REIHANEH | Issue Writer Certificate

Keywords

FEATURE SELECTIONQ2

CUSTOMER RELATIONSHIP MANAGEMENTQ2

REGRESSION DECISION TREEQ3

PURCHASING BEHAVIOR FORECASTINGQ3

CUSTOMER RECOGNITIONQ3

Abstract

In today’s world, CUSTOMER RELATIONSHIP MANAGEMENT is one of the most important fields for applying data mining. It can help us to predict each customer with what probability will purchase our product and it is useful for decision makers to access all customers effectively with developing various marketing strategies. The purpose of this study is to identify input features for making a CUSTOMER RECOGNITION system that is computationally efficient and effective. Although forecasting methods are popularly used, it cannot be useful unless irrelevant features are removed because they will present inappropriate CUSTOMER RECOGNITION and make poor results. In this paper, REGRESSION DECISION TREE is used for FEATURE SELECTION in order to design a CUSTOMER RECOGNITION system and forecasting future customers. The novelty of the method lies in the implementation of the mentioned approach in CUSTOMER RECOGNITION and forecasting field. A real case of the insurance data set in the Netherlands is used in order to indicate the application of the influencing set of features identification on customer purchasing behavior model. A significant reduction in the set of features is obtained. As a result, applying REGRESSION DECISION TREE, optimizes input features for forecasing. Also, the results show that reduction of dimensionality leads to decrease computation.

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APA: Copy

SOROUSH, ALIREZA, BAHREININEJAD, ARDESHIR, & GHASEM ESFAHANI, REIHANEH. (2011). APPLYING A PRUNED REGRESSION TREE FOR FEATURE SELECTION ON CUSTOMER PURCHASING BEHAVIOR FORECASTING IN INSURANCE INDUSTRY. IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH), 25(4 (100)), 131-152. SID. https://sid.ir/paper/100972/en

Vancouver: Copy

SOROUSH ALIREZA, BAHREININEJAD ARDESHIR, GHASEM ESFAHANI REIHANEH. APPLYING A PRUNED REGRESSION TREE FOR FEATURE SELECTION ON CUSTOMER PURCHASING BEHAVIOR FORECASTING IN INSURANCE INDUSTRY. IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH)[Internet]. 2011;25(4 (100)):131-152. Available from: https://sid.ir/paper/100972/en

IEEE: Copy

ALIREZA SOROUSH, ARDESHIR BAHREININEJAD, and REIHANEH GHASEM ESFAHANI, “APPLYING A PRUNED REGRESSION TREE FOR FEATURE SELECTION ON CUSTOMER PURCHASING BEHAVIOR FORECASTING IN INSURANCE INDUSTRY,” IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH), vol. 25, no. 4 (100), pp. 131–152, 2011, [Online]. Available: https://sid.ir/paper/100972/en

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