کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
404174 | 677395 | 2008 | 8 صفحه PDF | دانلود رایگان |

This paper presents a novel multi-layer perceptron using a non-additive decision making method and applies this model to the financial distress analysis, which is an important classification problem for a business, and the multi-layer perceptron has played a significant role in financial distress analysis. Traditionally, an activation function of an output neuron performs an additive method, namely the weighted sum method. Since the assumption of additivity among individual variables may not be reasonable, this paper uses a non-additive method, Choquet fuzzy integral, the fuzzy integral, to replace the weighted sum. In order to determine appropriate parameter specifications in the proposed model, a genetic algorithm is designed by considering the maximization of the number of correctly classified training patterns and the minimization of the training errors. The sample data obtained from Moody’s Industrial Manuals are employed to examine the classification ability of the proposed model. The results demonstrate that the proposed model performs well in comparison with the traditional multi-layer perceptron and some multivariate techniques.
Journal: Knowledge-Based Systems - Volume 21, Issue 5, July 2008, Pages 383–390