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On identifying potential direct marketing consumers using adaptive boosted support vector machine

Armin Lawi · Ali Akbar Velayaty · Zahir Zainuddin

24 citations0/0 files storedInternational Conference on Computer Applications and Information Processing Technology (CAIPT)

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Customer churn and segmentation Data Mining and Machine Learning Applications Financial Distress and Bankruptcy Prediction

Abstract

Identifying potential consumers for direct marketing to very large data is a difficult and impossible task to do manually. Therefore, the machine learning approach needs to help analyze the data to contribute in determining the marketing strategy policy. In this paper, vector machine support methods using the Adaboost algorithm are investigated to classify potential customers for direct marketing of large bank data marketing. The adaboost algorithm aims to build a better model than the model generated from a single classifier. Data obtained from UCI machine learning repository. Total data is processed as many as 9280 data with the number of attributes of 20 classes and 1 target class. Training data and test data are divided into 70% and 30%. This classification predicts the prospects for a deposit subscription. The results show that the SVM method using Adaboost algorithm obtained accuracy is 95.07% and the sensitivity is 91.65% higher than the ordinary SVM approach.

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2014A data-driven approach to predict the success of bank telemarketing 2011Using data mining for bank direct marketing: an application of the CRISP-DM methodology 2009Least squares support vector machines ensemble models for credit scoring 2014Bank Direct Marketing Analysis of Data Mining Techniques 2014Feature selection with data balancing for prediction of bank telemarketing

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2021Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset 2020Class balancing in customer segments classification using support vector machine rule extraction and ensemble learning

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