摘要
针对目前多数财务危机预警模型为静态建模、模型的增量学习能力不足等问题,设计了一种基于分类器集成的增量学习系统,利用财务类别导向知识对集成子系统进行动态选择,构建了具有增量学习能力的财务危机动态预警模型。基于实际财务数据的实验表明,基于增量学习系统的财务危机预警模型是一种兼具稳定性和适应性的财务危机预警工具。
In view of the problems existing in most of models of predicting financial distress, such as the static modeling, the lack of incremental learning ability and so on, an incremental learning system based on the classifier ensemble is designed. Financial category-oriented knowledge is applied to realize the dynamic selection for the classifier ensemble subsystem. Then the dynamic prediction model of financial distress with incre- mental learning ability is build. Model experiments show that, the prediction model of financial distress based on the incremental learning system is stable and adaptable,which is an effective method of predicting financial distress.
出处
《技术经济》
CSSCI
2012年第5期109-114,共6页
Journal of Technology Economics
基金
国家自然科学基金项目"函数型数据的统计推断"(11071120)
教育部人文社会科学研究规划基金项目"嵌入节能目标的震后灾区民用建筑重建工程质量评估体系及监管机制研究"(10YJA630020)
江苏省社会科学基金项目"江苏省文明城市测评体系研究"(08SHA001)
关键词
增量学习
财务危机
概念漂移
分类器集成
动态预警
incremental learnings financial distress
concept driclassifier ensemble
dynamic prediction
