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

Abstract

Construction firms are vulnerable to bankruptcy due to the complex nature of the industry, high competitions, the high risk involved, and considerable economic fluctuations. Thus, evaluating financial status and predicting business failures of construction companies are crucial for owners, general contractors, investors, banks, insurance firms, and creditors. The prediction results can be used to select qualified contractors capable of accomplishing the projects. In this study, a hybrid fuzzy instancebased 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 the firefly algorithm (FA). In this hybrid paradigm, the FKNC is utilized to classify the contractors into two groups ("default" and "nondefault") based on their past financial performances. Since the "nondefault" samples dominate the historical database, the SMOTE algorithm is employed to create synthetic samples of the minority class and therefore alleviates the between-class imbalance problem. Moreover, the FA is employed to determine an appropriate set of model parameters. Experimental results have shown that the proposed FICDP can outperform other benchmark methods.Index Terms-Default prediction, fuzzy instance based classifier, swarm intelligence, synthetic minority over-sampling technique.

Credit Risk Evaluation in Peer-to-peer Lending With Linguistic Data Transformation and Supervised Learning

“…In combination with traditional methodologies, the tools of fuzzy set theory have been applied to machine learning in various ways. Cheng and Hoang [31] propose a hybrid fuzzy instance based classifier for contractor default prediction (FICDP) to minimize risk of default related to selecting contractors. FICDP is used in the process of balancing the data set containing only a small number of defaults by oversampling this minority class.…”

Fuzzy Hybrid Computing in Construction Engineering and Management

“…However, the theory of fuzzy sets has been intensively investigated over the years, leading to the development of a variety of different fuzzy techniques in diverse fields of study, such as learning (Jiang, Deng, Chung, & Wang, 2017;Singh, Pal, Verma, & Vyas, 2017;Zuo, Zhang, Pedrycz, Behbood, & Lu, 2017), quality control (Kaya, Erdogan, & Yıldız, 2017;Kaya & Kahraman, 2011;Sentu¨rk, Erginel, Kaya, & Kahraman, 2014) and decision-making (Lourenzutti & Krohling, 2016;Roszkowska & Kacprzak, 2016;Tyagi, Agrawal, Yang, & Ying, 2017). In fact, the theory of fuzzy sets has also heavily contributed to research in the construction industry (Cheng & Hoang, 2015;Elbarkouky, Fayek, Siraj, & Sadeghi, 2016;Mirahadi & Zayed, 2016). This widespread application of fuzzy sets shows the unquestionable usefulness of fuzzy set theory.…”

Methodological approach of construction business failure prediction studies: a review

“…Critics of undersampling are sceptical, as mentioned in their research articles (e.g. Cheng and Hoang 2015), that deleted data might be those that are crucial to the learning/development process of any technique.…”