Reversing uncertainty sampling to improve active learning schemes

• Autores: Cardellino, Cristian; Alonso i Alemany, Laura
• Año de publicación: 2015
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Informáticas; active learning; pool-based uncertainty sampling; Aprendizaje
• Institución: Universidad Nacional de La Plata

Active learning provides promising methods to optimize the cost of manually annotating a dataset. However, practitioners in many areas do not massively resort to such methods because they present technical difficulties and do not provide a guarantee of good performance, especially in skewed distributions with scarcely populated minority classes and an undefined, catch-all majority class, which are very common in human-related phenomena like natural language. In this paper we present a comparison of the simplest active learning technique, pool-based uncertainty sampling, and its opposite, which we call reversed uncertainty sampling. We show that both obtain results comparable to the random, arguing for a more insightful approach to active learning.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)