Business Failure Prediction by Hybrid Data Mining Approach: A Case of Thailand Agribusiness
Keywords:
data mining, business failure, forecasting, early warning model, classificationAbstract
The failure of a business could significantly impact private companies, the government, and the whole economy. Therefore, predicting business failure is always one major research problem in business and economics. Many methods, such as theoretical models, statistical models, and data mining techniques, were applied to predict business failures. This research developed a business failure prediction model to classify failed and non-failed companies from one to three years before the failure by a hybrid data mining technique. The interest of this research is to integrate clustering and classification techniques to predict business failure, which can be beneficial for further research related to business failure prediction or early warning models. The study involved 3,118 agribusiness companies that submitted their financial statements from 2016 to 2020 in Thailand. Based on the data of financial statements, a single classifier, including decision tree (DT), logistic regression (LR), and neural network (NN), was compared with a hybrid data mining technique—clustering and classification. The results showed that applying the hybrid method, k-mean and DT, helped to improve the business failure prediction performance.
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เนื้อหาและข้อมูลที่ปรากฏในบทความที่ตีพิมพ์ในวารสารสถิติประยุกต์และเทคโนโลยีสารสนเทศถือเป็นความคิดเห็นส่วนบุคคลของผู้เขียนแต่ละท่าน ความผิดพลาดของข้อความและผลที่อาจเกิดจากนำข้อความเหล่านั้นไปใช้ผู้เขียนบทความจะเป็นผู้รับผิดชอบแต่เพียงผู้เดียว บทความ ข้อมูล เนื้อหา รูปภาพ ฯลฯ ที่ได้รับการตีพิมพ์ในวารสารถือเป็นลิขสิทธิ์ของวารสาร หากบุคคลหรือหน่วยงานใดต้องการนำทั้งหมดหรือส่วนหนึ่งส่วนใดไปเผยแพร่ต่อหรือเพื่อกระทำการใดๆ จะต้องได้รับอนุญาตเป็นลายลักอักษรณ์จากวารสาร ก่อนเท่านั้น
