In the construction industry, evaluating the financial status of a contractor is a challenging task due to the myriad of the input data as well as the complexity of the working environment. This article presents a novel hybrid intelligent approach named as Evolutionary Least Squares Support Vector Machine Inference Model for Predicting Contractor Default Status (ELSIM-PCDS). The proposed ELSIM-PCDS is established by hybridizing the Synthetic Minority Over-sampling Technique (SMOTE), Least Squares Support Vector Machine (LS-SVM), and Differential Evolution (DE) algorithms. In this new paradigm, the SMOTE is specifically used to deal with the imbalanced classification problem. The LS-SVM acts as a supervised learning technique for learning the classification boundary that separates the default and non-default contractors. Additionally, the DE algorithm automatically searches for the optimal parameters of the classification model. Experimental results have demonstrated that the classification performance of the ELSIM-PCDS is better than that of other benchmark methods. Therefore, the proposed hybrid approach is a promising alternative for predicting contractor default status. 
A novel hybrid intelligent approach for contractor default status prediction