Novel Bayesian inference on optimal parameters of support vector machines and its application to industrial survey data classification

Engineering Asset Management (EAM) is a recently attractive discipline and it aims to address valuable contributions of asset management to organization's success. As of today, there is no specific method to evaluate performance of EAM standards. This paper aims to fill this gap and rank performance of asset management automatically after conducting survey, instead of evaluating questionnaires, analyzing results and ranking performances with a tedious process. Hence, it is necessary to develop intelligent data classification to simplify the whole procedure. Among many supervised learning methods, support vector machine attracts much attention for binary classification problems and its extension, namely multiple support vector machines, is able to solve multiclass classification problems. It is crucial to find optimal parameters of support vector machines prior to their use for prediction of unknown testing data sets. In this paper, novel Bayesian inference on optimal parameters of support vector machines is proposed. Firstly, a state space model is constructed to find the relationship between parameters of support vector machines and guess cross-validation accuracy. Here, the guess cross-validation accuracy aims to prevent support vector machines from overfitting. Secondly, particle filter is introduced to iteratively find posterior probability density functions of the parameters of support vector machines. Then, optimal parameters of support vector machines can be found from the posterior probability density functions. Ultimately, survey data collected from industry are used to validate the effectiveness of the proposed Bayesian inference method. Comparisons with some randomly selected parameters are conducted to highlight the superiority of the proposed method. The results show that the proposed Bayesian inference method can result in both high training and testing accuracies. (C) 2016 Elsevier B.V. All rights reserved.