An Efficient Probability Estimation Decision Tree Postprocessing Method for Mining Optimal Profitable Knowledge for Enterprises with Multi-Class Customers

  • Janapati Naga Muneiah Research Scholar, Jawaharlal Nehru Technologial University, Kakinada Andhra Pradesh
  • Ch D V SubbaRao Professor, Department of Computer Science and Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh
Keywords: Data mining, Knowledge Engineering and Applications, Machine Learning: Methods and Applications, actionable knowledge discovery, profit maximization

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.

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Published
2019-11-14
How to Cite
Naga Muneiah, J., & SubbaRao, C. (2019). An Efficient Probability Estimation Decision Tree Postprocessing Method for Mining Optimal Profitable Knowledge for Enterprises with Multi-Class Customers. Inteligencia Artificial, 22(64), 63-84. https://doi.org/10.4114/intartif.vol22iss64pp63-84