Controlling false positives in multiple instance learning : The "c-rule" approach
Delgado de la Torre, Rosario (Universitat Autònoma de Barcelona. Departament de Matemàtiques)

Date:	2025
Abstract:	This paper introduces a novel strategy for labeling bags in binary Multiple Instance Learning (MIL) under the standard MI assumption. The proposed approach addresses errors in instance labeling by classifying a bag as positive if it contains at least c positively labeled instances. This strategy seeks to balance the trade-off between controlling the false positive rate (mislabeling a negative bag as positive) and the false negative rate (mislabeling a positive bag as negative) while reducing labeling efforts. The study provides theoretical justifications for this approach and introduces algorithms for its implementation, including determining the minimum value of c required to keep error rates below predefined thresholds. Additionally, it proposes a methodology to estimate the number of genuinely positive and negative instances within bags. Simulations demonstrate the superior performance of the "c-rule" compared to the standard rule (corresponding to ) in scenarios with sparse positive bags and moderately low to high probability of misclassifying a negative instance. This trend is further validated through comparisons using two real-world datasets. Overall, this research advances the understanding of error management in MIL and provides practical tools for real-world applications.
Grants:	Agencia Estatal de Investigación PID2021-123733NB-I00
Note:	Altres ajuts: acords transformatius de la UAB
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Published in:	International Journal of Approximate Reasoning, Vol. 179 (April 2025) , art. 109367, ISSN 1873-4731

DOI: 10.1016/j.ijar.2025.109367

Available from: 2027-04-30 Postprint

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