Applying support vector machines and mutual information to book losses prediction

This work presents a feasible solution to the problem of book losses prediction from financial and general data in companies. The specific problem tackled in this work corresponds to a real dataset of Spanish companies. A Mutual Information-based criterion has been applied in order to reduce the initial set of variables, and a Support Vector Machine classifier has been designed to perform the prediction. The results show that the proposed approach obtains an important reduction of the number of variables needed to perform the prediction, improving the generalization capabilities of the model. The accuracy rates were above the 84% in the test set, much better than those obtained by other soft-computing algorithms (such as Genetic Programming, Self-Organizing Maps or Artificial Neural Networks) working with the same dataset and presented in previous works. The proposed approach shows to be promising and could be determinant in providing the experts with the right tools for the selection of the relevant factors and for the prediction in this difficult problem.