Evaluating Contractor Financial Status Using a Hybrid Fuzzy Instance Based Classifier: Case Study in the Construction Industry

PROJECT TITLE :

Evaluating Contractor Financial Status Using a Hybrid Fuzzy Instance Based Classifier: Case Study in the Construction Industry

ABSTRACT:

Construction corporations are liable to bankruptcy thanks to the complex nature of the business, high competitions, the high risk concerned, and considerable economic fluctuations. Therefore, evaluating money standing and predicting business failures of construction companies are crucial for homeowners, general contractors, investors, banks, insurance companies, and creditors. The prediction results can be used to pick out qualified contractors capable of accomplishing the comes. During this study, a hybrid fuzzy instance-based mostly classifier for contractor default prediction (FICDP) is proposed. The new approach is constructed by incorporating the fuzzy K-nearest neighbor classifier (FKNC), the synthetic minority over-sampling technique (SMOTE), and also the firefly algorithm (FA). During this hybrid paradigm, the FKNC is utilized to classify the contractors into 2 groups (“default” and “nondefault”) primarily based on their past financial performances. Since the “nondefault” samples dominate the historical database, the SMOTE algorithm is used to form artificial samples of the minority class and so alleviates the between-category imbalance downside. Moreover, the FA is employed to work out an acceptable set of model parameters. Experimental results have shown that the proposed FICDP will outperform other benchmark methods.

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