Classification algorithm based on pairwise comparison of features

Корноушенко, Е. К.

doi:10.1134/s000511791711011x

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Method (train-to-test ratios) Average accuracy (%) Kalyan Nagaraj, Amulyashree Sridhar (2015) [40] Logistic Regression (2/3-1/3) 97.2 Kalyan Nagaraj, Amulyashree Sridhar (2015) [40] Rotation Forest (2/3-1/3) 97.4 Kalyan Nagaraj, Amulyashree Sridhar (2015) [40] Naive Bayes (2/3-1/3) 98.3 Kalyan Nagaraj, Amulyashree Sridhar (2015) [40] RBF-based SVM (2/3-1/3) 99.6 E. K. Kornoushenko (2017) [41] Nearest Neighborhood (50%-50%) 97.6 J. Uthayakumar et.…”

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confidence: 99%

A Differential Evolution‐Oriented Pruning Neural Network Model for Bankruptcy Prediction

Tang

Zhu

et al. 2019

Complexity

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Financial bankruptcy prediction is crucial for financial institutions in assessing the financial health of companies and individuals. Such work is necessary for financial institutions to establish effective prediction models to make appropriate lending decisions. In recent decades, various bankruptcy prediction models have been developed for academics and practitioners to predict the likelihood that a loan customer will go bankrupt. Among them, Artificial Neural Networks (ANNs) have been widely and effectively applied in bankruptcy prediction. Inspired by the mechanism of biological neurons, we propose an evolutionary pruning neural network (EPNN) model to conduct financial bankruptcy analysis. The EPNN possesses a dynamic dendritic structure that is trained by a global optimization learning algorithm: the Adaptive Differential Evolution algorithm with Optional External Archive (JADE). The EPNN can reduce the computational complexity by removing the superfluous and ineffective synapses and dendrites in the structure and is simultaneously able to achieve a competitive classification accuracy. After simplifying the structure, the EPNN can be entirely replaced by a logic circuit containing the comparators and the logic NOT, AND, and OR gates. This mechanism makes it feasible to apply the EPNN to bankruptcy analysis in hardware implementations. To verify the effectiveness of the EPNN, we adopt two benchmark datasets in our experiments. The experimental results reveal that the EPNN outperforms the Multilayer Perceptron (MLP) model and our previously developed preliminary pruning neural network (PNN) model in terms of accuracy, convergence speed, and Area Under the Receiver Operating Characteristics (ROC) curve (AUC). In addition, the EPNN also provides competitive and satisfactory classification performances in contrast with other commonly used classification methods.

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