Determining critical factors for the success of machine learning libraries considering fuzzy interrelationships

Abstract

Advances in Machine Learning have led to groundbreaking developments across various fields. A key driver of this progress is the growing capabilities of machine learning libraries. However, these libraries still face certain limitations, which can hinder their application in some areas. In particular, developers often need to integrate their solutions across different libraries, languages, and platforms, which can reduce the effectiveness of machine learning applications. This study examines the direct and indirect impacts of various factors on the success of machine learning libraries, leveraging expert evaluations. Focusing on critical factors that shape library development based on user experiences is crucial. This approach ensures strategic advancement and optimizes energy and resource use. Given that many of these factors involve human perception-related uncertainties, the methodology incorporates a fuzzy linguistic approach with z-numbers to address the inherent vagueness. The findings reveal that considering the interrelationships among factors significantly alters their importance, as it accounts for both direct and indirect influences. Moreover, these interrelationships highlight which factors should be strategically prioritized, as improvements in one factor can drive improvements in others. The results obtained can support guiding the community developing artificial intelligence libraries toward the right objectives and formulating effective strategies to ensure the sustainability of artificial intelligence transformation.

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