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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52310

Title: A fast and accurate approach for bankruptcy forecasting using squared logistics loss with GPU-based extreme gradient boosting
Authors: Tuong Le
Bay Vo
Hamido Fujita
Ngoc-Thanh Nguyen
Sung Wook Baik
Contributors: 國立臺灣海洋大學:資訊工程學系
Date: 2019-08
Issue Date: 2019-11-18T01:22:24Z
Publisher: Information Sciences
Abstract: Abstract: Over the last two decades, the diagnosis of bankruptcy firms has become extremely important to business owners, banks, governments, securities investors, and economic stakeholders, in order to hedge against financial damage. This topic has attracted the attention of many computer scientists, as well as the financial sector. In computer science, a predictive model will be developed to analyse a firm's financial statements to predict its fate in the future based on the existing bankruptcy dataset. Many machine learning models have been developed to predict bankruptcy using specific datasets. This manuscript proposes a fast and accurate approach that utilises GPU-based extreme gradient boosting machine using squared logistics loss (SqLL) denoted by gXGBS for bankruptcy forecasting on both scenarios, including an imbalanced dataset (Korean bankruptcy dataset [KRBDS]) and two balanced datasets (USA bankruptcy dataset [USABDS] and Japanese bankruptcy dataset [JPNBDS]). To improve the performance of this prediction task, the study uses a custom loss function, namely SqLL, for an extreme gradient boosting machine to propose XGBS algorithm. Then, this study employs a GPU-based implementation of a decision tree construction algorithm (gDTC) to accelerate the processing time of XGBS. This is called a gXGBS algorithm. In addition, we also utilise a histogram-based tree construction algorithm using multi-GPU (gHTC), an approximation algorithm, for a gXGBS algorithm that will propose the third algorithm, namely gXGBS_hist, for the large datasets. The comprehensive experiments were conducted on three experimental datasets to evaluate the proposed approaches. The experimental results indicate that our proposed approaches outperform the state-of-the-art machine learning approaches for bankruptcy forecasting in terms of geometric mean (G-mean), Area Under the Receiver Operating Characteristic Curve (AUC) and processing time for all experimental datasets.
Relation: 494 pp.294-310
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52310
Appears in Collections:[資訊工程學系] 期刊論文

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