A multiple kernel learning-based decision support model for contractor pre-qualification

Due to the complex nature of the contractor pre-qualification such as subjectivity, non-linearity and multi-criteria, advanced model should be required for achieving a high accuracy of this decision-making process. Previous studies have been conducted to build up quantitative decision models for contractor pre-qualification, among them artificial neural network (ANN) and support vector machine (SVM) have been proved to be desirable in solving the pre-qualification problem with regards to their higher accuracy and efficiency for solving the non-linear problem of classification. Based on the algorithm of SVM, multiple kernel learning (MKL) method was developed and it has been proved to perform better than SVM in other areas. Hence, MKL is proposed in this research, the capability of MKL was compared with SVM through a case study. From the result, it has been proved that both SVM and MKL perform well in classification, and MKL is more preferable than SVM, with a proper parameter setting. Therefore, MKL can enhance the decision making of contractor pre-qualification.

Research Highlights
► We set up a MKL model for contractor pre-qualification.
► We compare the model of MKL and SVM, so as to find a more desirable one.
► The model solves the problems of subjectivity, non-linearity and multi-criteria of pre-qualification.