Adaptive Learning of Contractor Default Prediction Model for Surety Bonding

Abstract

The performance of a fuzzy expert system (FES) is significantly affected by the accuracy of its knowledge base parameters (membership functions and rule bases). The main contribution of this paper is in presenting a methodology to integrate an FES with adaptation/optimization techniques and applying the data-based adaptive learning concept to increase the accuracy of an FES developed for contractor default prediction for surety bonding. In addition, this paper investigates two optimization approaches (genetic algorithms and neural network back-propagation) for adaptation of fuzzy membership function (MBF) and rules’ degree of support (DoS) to determine the most suitable technique to adapt the FES. The optimized FES, called SuretyQualification, was validated using 30 hypothetical contractor default prediction cases, and the highest accuracy of the system (adapted using neural networks) was found to be 91.83%. Another contribution of this paper is the development of a software tool called SuretyQualification that provides a comprehensive and systematic evaluation process to evaluate a contractor and their risk of default on a project. The presented optimization approaches address FES context adaptation using any changing information conveyed by the input-output data and provide a methodology for continuous adaptation of the FES parameters, using practical cases to adjust the FES according to any contexts changes.