An Efficient Probability Estimation Decision Tree Postprocessing Method for Mining Optimal Profitable Knowledge for Enterprises with Multi-Class Customers
Open Access
- 14 November 2019
- journal article
- research article
- Published by IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial in INTELIGENCIA ARTIFICIAL
- Vol. 22 (64), 63-84
- https://doi.org/10.4114/intartif.vol22iss64pp63-84
Abstract
Enterprises often classify their customers based on the degree of profitability in decreasing order like C1, C2, ..., Cn. Generally, customers representing class Cn are zero profitable since they migrate to the competitor. They are called as attritors (or churners) and are the prime reason for the huge losses of the enterprises. Nevertheless, customers of other intermediary classes are reluctant and offer an insignificant amount of profits in different degrees and lead to uncertainty. Various data mining models like decision trees, etc., which are built using the customers’ profiles, are limited to classifying the customers as attritors or non-attritors only and not providing profitable actionable knowledge. In this paper, we present an efficient algorithm for the automatic extraction of profit-maximizing knowledge for business applications with multi-class customers by postprocessing the probability estimation decision tree (PET). When the PET predicts a customer as belonging to any of the lesser profitable classes, then, our algorithm suggests the cost-sensitive actions to change her/him to a maximum possible higher profitable status. In the proposed novel approach, the PET is represented in the compressed form as a Bit patterns matrix and the postprocessing task is performed on the bit patterns by applying the bitwise AND operations. The computational performance of the proposed method is strong due to the employment of effective data structures. Substantial experiments conducted on UCI datasets, real Mobile phone service data and other benchmark datasets demonstrate that the proposed method remarkably outperforms the state-of-the-art methods.Keywords
This publication has 22 references indexed in Scilit:
- Actionable strategies in three-way decisionsKnowledge-Based Systems, 2017
- Customer churn prediction in the telecommunication sector using a rough set approachNeurocomputing, 2017
- Extracting optimal actionable plans from additive tree modelsFrontiers of Computer Science, 2017
- Developing a prediction model for customer churn from electronic banking services using data miningFinancial Innovation, 2016
- Profit Maximization Analysis Based on Data Mining and the Exponential Retention Model Assumption with Respect to Customer Churn ProblemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Optimal Action Extraction for Random Forests and Boosted TreesPublished by Association for Computing Machinery (ACM) ,2015
- Actionable knowledge discovery and deliveryWIREs Data Mining and Knowledge Discovery, 2012
- Credit card churn forecasting by logistic regression and decision treeExpert Systems with Applications, 2011
- Top 10 algorithms in data miningKnowledge and Information Systems, 2007
- Choice Models and Customer Relationship ManagementMarketing Letters, 2005