Bankruptcy forecasting using case-based reasoning: The CRePERIE approach

被引：32

作者：

Sartori, Fabio ^{[1
]}

Mazzucchelli, Alice ^{[2
]}

Di Gregorio, Angelo ^{[2
]}

机构：

[1] Univ Milano Bicocca, Dept Comp Sci Syst & Commun, Viale Sarca 336-14, I-20126 Milan, Italy

[2] Univ Milano Bicocca, Dept Business Adm Finance Management & Law, Via Bicocca Arcimboldi 8, I-20126 Milan, Italy

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2016年 / 64卷

关键词：

Case-based reasoning; Bankruptcy prediction; Overall similarity; ARTIFICIAL NEURAL-NETWORKS; PREDICTION EMPIRICAL-EVIDENCE; SUPPORT VECTOR MACHINES; GENETIC ALGORITHMS; GENERAL FRAMEWORK; FINANCIAL RATIOS; SIMILARITY; PERFORMANCE; ENSEMBLE; MODEL;

D O I：

10.1016/j.eswa.2016.07.033

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Bankruptcy prediction is a very important research trend: although statistical methods are mainly used in literature, techniques based on Artificial Intelligence are interesting from many points of view. Among them, Case-Based Reasoning (CBR) could be useful to cluster enterprises according to opportune similarity metrics as well as suggest proper actions to take for avoiding bankruptcy in border-line situations. In this paper, we present a new and still under development CBR approach to this problem, that seems to return better results than previous attempts. The approach is based on different kinds of similarity metrics and is focused on the implementation of innovative revise algorithms. In particular, the paper shows how the revise step is crucial to improve the accuracy of the bankruptcy prediction model. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：400 / 411

页数：12

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