Machine learning vs. neutrosophic machine learning

摘要

Machine Learning (ML) and Neutrosophic Machine Learning (NML) represent two distinct paradigms in the realm of artificial intelligence, each addressing unique challenges in data processing, decision-making, and uncertainty modeling. ML, rooted in classical statistical and probabilistic principles, excels in tasks ranging from classification to recommendation systems. Its strength lies in the ability to discern patterns from labeled data and make predictions based on learned relationships. However, ML faces limitations in handling complex uncertainties beyond probabilistic representations. On the other hand, NML extends the ML framework by incorporating neutrosophic sets and logic. This introduces a third truth value, allowing for the explicit representation of truth, indeterminacy, and falsity within the data. NML is particularly adept at handling uncertainties, vagueness, and inconsistencies in various applications, such as medical diagnosis, robotics, and decision support systems. While ML models may lack interpretability, NML provides a more structured and transparent framework, offering insights into the decision-making process, especially in subjective and uncertain domains. The comparison between ML and NML involves trade-offs. ML, with its versatility and widespread adoption, is effective in tasks where uncertainties are primarily probabilistic. NML, with its focus on explicit uncertainty representation, is tailored for applications where indeterminacy and vagueness play a pivotal role. Understanding the strengths and limitations of each paradigm is crucial in selecting the appropriate approach based on the specific requirements of a given task. This chapter provides a deep insight into ML and NML and compares them with relevant parameters.