Voting features based classifier with feature construction and its application to predicting financial distress
| Autor: | Murat Cakir, H. Altay Güvenir |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Computer science
media_common.quotation_subject Financial ratio Classification features 02 engineering and technology Financial Distress Machine learning computer.software_genre Classification algorithm 03 medical and health sciences Artificial Intelligence Voting 0202 electrical engineering electronic engineering information engineering 030304 developmental biology media_common 0303 health sciences business.industry General Engineering Feature Projections Classification Feature Construction Computer Science Applications Data set Statistical classification 020201 artificial intelligence & image processing Financial distress Artificial intelligence Data mining business computer Classifier (UML) |
| Zdroj: | Expert Systems with Applications Expert Systems with Applications: an international journal |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2009.06.037 |
| Popis: | Cataloged from PDF version of article. Voting features based classifiers. shortly VFC. have been shown to perform well on most real-world data sets They are robust to irrelevant features and missing feature values. In this paper, we introduce an extension to VFC. called voting features based classifier with feature construction, VFCC for short, and show its application to the problem of predicting if a bank will encounter financial distress by analyzing, current financial statements. The previously developed VFC learn a set of rules that contain a single condition based on a single feature in their antecedent. The VFCC algorithm proposed in this work, oil the other hand, constructs rules whose antecedents may contain conjuncts based on several features. Experimental results on recent financial ratios of banks in Turkey show that the VFCC algorithm achieves better accuracy than other well-known rule learning classification algorithms (C) 2009 Elsevier Ltd. All rights reserved |
| Databáze: | OpenAIRE |
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