An Adaptation of the Input Doubling Method for Solving Classification Tasks in Case of Small Data Processing

Ivan Izonin, Roman Tkachenko, Myroslav Havryliuk, Michal Gregus, Pavlo Yendyk, Yaroslav Tolstyak

Abstract: In the era of big data processing, numerous techniques prove valuable for analyzing large-scale datasets. However, the efficient processing of small data is equally crucial, particularly in the context of intelligent mobile systems. This importance stems from resource constraints, real-time requirements, privacy concerns, and the emergence of edge computing paradigms. In this paper, we adapt an existing input doubling method to address classification tasks when facing limited training datasets. The proposed solution is implemented by modifying the procedure for aggregating predicted values, based on the principles of majority voting. This approach differs significantly from the existing method, which relies on averaging prediction results. A flowchart of the adapted method is presented, detailing the procedures for training and application based on the use of a plurality voting scheme. Modeling is conducted on a short, imbalanced dataset for solving express diagnostic tasks in the field of transplantology. The results demonstrate the high effectiveness of employing the adapted input doubling method for classification tasks in transplantology, where acquiring a substantial training dataset for analysis is challenging.