Extracting Features from Optimization Techniques to Discover Liability of Customers Using Decision Trees Senthil Vadivu P.1,*, David Vasantha Kalyani Dr. (Mrs.)2,** 1Head, Department Of Computer Applications, Hindusthan College of Arts and Science, Coimbatore-641028. 2Associate Professor, Department of Computer Science, Avinashilingam Deemed University, Coimbatore, Tamil Nadu, India.. *email id: sowju_sashi@rediffmail.com
**email id: vasanthadavid@yahoo.com.
Abstract Most algorithms based on data mining are used to discover customer models for distributing information, which is used in Customer Relationship Management (CRM), for pointing out customers who are loyal and who are attritors, but human expertise is a must for discovering knowledge manually. Many post processing techniques have been introduced that do not suggest action to increase the objective function such as profit. In this paper, a feature extraction technique is proposed for the best approximation property. An Uncorrelated Discriminant Analysis (UDA) algorithm based on Maximum Margin Criterion (MMC) is used. The extracted features via UDA are statistically uncorrelated. It serves as an effective solution for small sample size problem. The extracted features are given as an input to a novel algorithm that suggests actions to change the customer from the undesired status to the desired one. These algorithms can discover the reduction in cost and transform customer from undesirable classes to desirable ones. The UDA algorithm is evaluated in terms of classification accuracy and robustness. Many tests have been conducted and experimental results have been analyzed in this paper. Top Keywords Customer Relationship Management, Bounded Segmentation Problem, Feature Extraction Ant Colony Optimizsation, Maximum Margin Criterion, Uncorrelated Discriminant Analysis, Decision Trees, Attrition. Top |