Integration of Multivariate Beta-based Hidden Markov Models and Support Vector Machines with Medical Applications

Abstract

In this paper, we propose a novel hybrid discriminative generative model by integrating a modified version of hidden Markov model (HMM), multivariate Beta-based HMM with support vector machine (SVM). We apply Fisher Kernel to define decision boundary and separate classes. In this model, we assume that HMM emission probabilities follow a Beta mixture model as generalizing the assumption of Gaussianity may not be practical in modeling real-world applications. HMM as a generative model needs less amount of data however, its accuracy is less than discriminative models such as SVM. Moreover, in some applications, data may have various feature-length. We tackle this problem with Fisher Kernel. We apply our proposed model to medical applications, lung cancer detection, colonoscopy image, and colon tissue analysis. The results indicate that our proposed model could be a promising alternative.