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Full-Text Articles in Applied Statistics
A Comparative Analysis Of Decision Trees Vis-À-Vis Other Computational Data Mining Techniques In Automotive Insurance Fraud Detection, Adrian Gepp, Kuldeep Kumar, J Holton Wilson, Sukanto Bhattacharya
A Comparative Analysis Of Decision Trees Vis-À-Vis Other Computational Data Mining Techniques In Automotive Insurance Fraud Detection, Adrian Gepp, Kuldeep Kumar, J Holton Wilson, Sukanto Bhattacharya
Kuldeep Kumar
No abstract provided.
A Comparative Analysis Of Decision Trees Vis-À-Vis Other Computational Data Mining Techniques In Automotive Insurance Fraud Detection, Adrian Gepp, Kuldeep Kumar, J Holton Wilson, Sukanto Bhattacharya
A Comparative Analysis Of Decision Trees Vis-À-Vis Other Computational Data Mining Techniques In Automotive Insurance Fraud Detection, Adrian Gepp, Kuldeep Kumar, J Holton Wilson, Sukanto Bhattacharya
Adrian Gepp
No abstract provided.
Model Development Techniques And Evaluation Methods For Prediction And Classification Of Consumer Risk In The Credit Industry, Jennifer Priestley, Satish Nargundkar
Model Development Techniques And Evaluation Methods For Prediction And Classification Of Consumer Risk In The Credit Industry, Jennifer Priestley, Satish Nargundkar
Jennifer L. Priestley
In this chapter, we examine and compare the most prevalent modeling techniques in the credit industry, Linear Discriminant Analysis, Logistic Analysis and the emerging technique of Neural Network modeling. K-S Tests and Classification Rates are typically used in the industry to measure the success in predictive classification. We examine those two methods and a third, ROC Curves, to determine if the method of evaluation has an influence on the perceived performance of the modeling technique. We found that each modeling technique has its own strengths, and a determination of the “best” depends upon the evaluation method utilized and the costs …