Improving sale performance prediction using support vector machines

Authors:
David Delgado-GóMez;David Aguado;Jorge Lopez-Castroman;Carlos Santacruz;Antonio ArtéS-Rodriguez
Affiliations:
Department of Statistics, Carlos III University, Madrid, Spain;Institute of Knowledge Engineering, Autonomous University, Madrid, Spain;Department of Psychiatry, Fundacion Jimenez Diaz, Madrid, Spain;Institute of Knowledge Engineering, Autonomous University, Madrid, Spain;Department of Signal and Communications Theory, Carlos III University, Madrid, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In this article, an expert system based on support vector machines is developed to predict the sale performance of some insurance company candidates. The system predicts the performance of these candidates based on some scores, which are measurements of cognitive characteristics, personality, selling skills and biodata. An experiment is conducted to compare the accuracy of the proposed system with respect to previously reported systems which use discriminant functions or decision trees. Results show that the proposed system is able to improve the accuracy of a baseline linear discriminant based system by more than 10% and that also exceeds the state of the art systems by almost 5%. The proposed approach can help to reduce considerably the direct and indirect expenses of the companies.