2011
Delgado-Gómez, David; Aguado, David; Lopez-Castroman, Jorge; Santacruz, Carlos; Artés-Rodríguez, Antonio
Improving Sale Performance Prediction Using Support Vector Machines Artículo de revista
En: Expert Systems with Applications, vol. 38, no 5, pp. 5129–5132, 2011.
Resumen | Enlaces | BibTeX | Etiquetas: Recruitment process, Sale performance prediction, Support vector machines
@article{Delgado-Gomez2011a,
title = {Improving Sale Performance Prediction Using Support Vector Machines},
author = {David Delgado-G\'{o}mez and David Aguado and Jorge Lopez-Castroman and Carlos Santacruz and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P41_2011_Improving Sale Performance Prediction Using Support Vector Machines.pdf
http://www.sciencedirect.com/science/article/pii/S0957417410011322},
year = {2011},
date = {2011-01-01},
journal = {Expert Systems with Applications},
volume = {38},
number = {5},
pages = {5129--5132},
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.},
keywords = {Recruitment process, Sale performance prediction, Support vector machines},
pubstate = {published},
tppubtype = {article}
}
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.