...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Engineering Management, IEEE Transactions on >Evaluating Contractor Financial Status Using a Hybrid Fuzzy Instance Based Classifier: Case Study in the Construction Industry
【24h】

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

机译：使用基于混合模糊实例的分类器评估承包商的财务状况：建筑行业的案例研究

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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 instance-based classifier for contractor default prediction (FICDP) is proposed. The new approach is constructed by incorporating the fuzzy -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.
机译：由于行业的复杂性，激烈的竞争，高风险以及巨大的经济波动，建筑公司很容易破产。因此，评估建筑公司的财务状况并预测其倒闭对业主，总承包商，投资者，银行，保险公司和债权人至关重要。预测结果可用于选择能够完成项目的合格承包商。在这项研究中，提出了一种用于承包商违约预测的混合基于模糊实例的分类器（FICDP）。通过结合模糊近邻分类器（FKNC），合成少数过采样技术（SMOTE）和萤火虫算法（FA）构造新方法。在这种混合范式中，FKNC用于根据承包商过去的财务表现将其分为两类（“默认”和“非默认”）。由于“非默认”样本在历史数据库中占主导地位，因此采用SMOTE算法来创建少数类别的综合样本，从而缓解了类别之间的不平衡问题。此外，FA用于确定一组适当的模型参数。实验结果表明，提出的FICDP可以胜过其他基准测试方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号