An Evolutionary Programming Based SVM Ensemble Model for Corporate Failure Prediction
نویسندگان
چکیده
In this study, a multistage evolutionary programming (EP) based support vector machine (SVM) ensemble model is proposed for designing a corporate bankruptcy prediction system to discriminate healthful firms from bad ones. In the proposed model, a bagging sampling technique is first used to generate different training sets. Based on the different training sets, some different SVM models with different parameters are then trained to formulate different classifiers. Finally, these different SVM classifiers are aggregated into an ensemble output using an EP approach. For illustration, the proposed SVM ensemble model is applied to a real-world corporate failure prediction problem.
منابع مشابه
Development of an Ensemble Multi-stage Machine for Prediction of Breast Cancer Survivability
Prediction of cancer survivability using machine learning techniques has become a popular approach in recent years. In this regard, an important issue is that preparation of some features may need conducting difficult and costly experiments while these features have less significant impacts on the final decision and can be ignored from the feature set. Therefore, developing a machine for p...
متن کاملPREDICTION OF SLOPE STABILITY STATE FOR CIRCULAR FAILURE: A HYBRID SUPPORT VECTOR MACHINE WITH HARMONY SEARCH ALGORITHM
The slope stability analysis is routinely performed by engineers to estimate the stability of river training works, road embankments, embankment dams, excavations and retaining walls. This paper presents a new approach to build a model for the prediction of slope stability state. The support vector machine (SVM) is a new machine learning method based on statistical learning theory, which can so...
متن کاملApplication of ensemble learning techniques to model the atmospheric concentration of SO2
In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...
متن کاملAn efficient and effective ensemble of support vector machines for anti-diabetic drug failure prediction
The treatment of patients with type 2 diabetes is mostly based on drug therapies, aiming at managing glucose levels appropriately. As the number of patients with type 2 diabetes continually increases worldwide, predicting drug treatment failure becomes an important issue. Support vector machine (SVM) can be a good method for the anti-diabetic drug failure prediction problem; however, it is diff...
متن کاملSimulation and prediction of scour whole dimensions downstream of siphon overflow using support vector machine and Gene expression programming algorithms
Background and Objectives: The purpose of this study is to simulate and predict the dimensions of the scour cavity downstream of the siphon overflow using the SVM model and compare it with other numerical methods. The use of the SVM algorithm as a meta-heuristic system in simulating complex processes in which the dependent variable is a function of several independent variables has been widely ...
متن کامل