An Adaptive Fuzzy k-Nearest Neighbor Method Based on Parallel Particle Swarm Optimization for Bankruptcy Prediction
This study proposes an efficient non-parametric classifier for bankruptcy prediction using an adaptive fuzzy k-nearest neighbor (FKNN) method, where the nearest neighbor k and the fuzzy strength parameter m are adaptively specified by the particle swarm optimization (PSO) approach. In addition to performing the parameter optimization for FKNN, PSO is utilized to choose the most discriminative subset of features for prediction as well. Time varying acceleration coefficients (TVAC) and inertia weight (TVIW) are employed to efficiently control the local and global search ability of PSO. Moreover, both the continuous and binary PSO are implemented in parallel on a multi-core platform. The resultant bankruptcy prediction model, named PTVPSO-FKNN, is compared with three classification methods on a real-world case. The obtained results clearly confirm the superiority of the developed model as compared to the other three methods in terms of Classification accuracy, Type I error, Type II error and AUC (area under the receiver operating characteristic (ROC) curve) criterion. It is also observed that the PTVPSO-FKNN is a powerful feature selection tool which has indentified a subset of best discriminative features. Additionally, the proposed model has gained a great deal of efficiency in terms of CPU time owing to the parallel implementation.
KeywordsFuzzy k-nearest neighbor Parallel computing Particle swarm optimization Feature selection Bankruptcy prediction
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