Using Hybrid Discriminative-Generative Models for Binary Classification

• Published in: Automatic control and computer sciences Vol. 53; no. 4; pp. 320 - 327
• Main Author: Abroyan, N.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.07.2019; Springer Nature B.V
• Subjects: Algorithms; Classification; Computer Science; Control Structures and Microprogramming; Machine learning; generative algorithms; hybrid model; discriminative algorithms; machine learning; classification
• ISSN: 0146-4116; 1558-108X
• DOI: 10.3103/S0146411619040023

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.