Using Hybrid Discriminative-Generative Models for Binary Classification

Discriminative and generative machine learning algorithms have been successfully used in different classification tasks during the last several decades. They both have some advantages and disadvantages and depending on a problem, one type of algorithm performs better than the other one. In this paper we contribute to the research of combination of both approaches and propose literature based a hybrid discriminative-generative generic model. Also, we propose hybrid model structure finding and building a new algorithm. We present theoretical and practical advantages of the hybrid model over its consisting algorithms, efficiency of the model structure finding algorithm, then perform experiments and compare results.