A Model for Recognizing Key Factors and Applications Thereof to Engineering.

This paper presents an approach to recognize key factors in data classification. Using collinearity diagnostics to delete the factors of repeated information and Logistic regression significant discriminant to select the factors which can effectively distinguish the two kinds of samples, this paper creates a model for recognizing key factors. The proposed model is demonstrated by using the 2044 observations in finical engineering. The experimental results demonstrate that the 13 indicators such as "marital status," "net income of borrower," and "Engel's coefficient" are the key factors to distinguish the good customers from the bad customers. By analyzing the experimental results, the performance of the proposed model is verified. Moreover, the proposed method is simple and easy to be implemented. [ABSTRACT FROM AUTHOR]