Journal
KNOWLEDGE-BASED SYSTEMS
Volume 24, Issue 8, Pages 1348-1359Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2011.06.008
Keywords
Fuzzy k-nearest neighbor; Parallel computing; Particle swarm optimization; Feature selection; Bankruptcy prediction
Categories
Funding
- National Natural Science Foundation of China (NSFC) [60873149, 60973088, 60773099]
- National High-Tech Research and Development Plan of China [2006AA10Z245, 2006AA10A309]
- Shanghai Key Laboratory of Intelligent Information Processing in Fudan University [IIPL-09-007]
- National Laboratory of Pattern Recognition (NLPR)
- Chinese Ministry of Education
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Bankruptcy prediction is one of the most important issues in financial decision-making. Constructing effective corporate bankruptcy prediction models in time is essential to make companies or banks prevent bankruptcy. This study proposes a novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor (FKNN) method, where the neighborhood size k and the fuzzy strength parameter m are adaptively specified by the continuous particle swarm optimization (PSO) approach. In addition to performing the parameter optimization for FKNN, PSO is also utilized to choose the most discriminative subset of features for prediction. Adaptive control parameters including time-varying acceleration coefficients (TVAC) and time-varying inertia weight (TVIW) are employed to efficiently control the local and global search ability of PSO algorithm. Moreover, both the continuous and binary PSO are implemented in parallel on a multi-core platform. The proposed bankruptcy prediction model, named PTVPSO-FKNN, is compared with five other state-of-the-art classifiers on two real-life cases. The obtained results clearly confirm the superiority of the proposed model in terms of classification accuracy, Type I error, Type II error and area under the receiver operating characteristic curve (AUC) criterion. The proposed model also demonstrates its ability to identify the most discriminative financial ratios. Additionally, the proposed model has reduced a large amount of computational time owing to its parallel implementation. Promisingly, PTVPSO-FKNN might serve as a new candidate of powerful early warning systems for bankruptcy prediction with excellent performance. (C) 2011 Elsevier B.V. All rights reserved.
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