mi-DS: Multiple-Instance Learning Algorithm.
Multiple-instance learning (MIL) is a supervised learning technique that addresses the problem of classifying bags of instances instead of single instances. In this paper, we introduce a rule-based MIL algorithm, called mi-DS, and compare it with 21 existing MIL algorithms on 26 commonly used data sets. The results show that mi-DS performs on par with or better than several well-known algorithms and generates models characterized by balanced values of precision and recall. Importantly, the introduced method provides a framework that can be used for converting other rule-based algorithms into MIL algorithms.
Dat T
DT
Nguyen
Cao D
CD
Nguyen
Rosalyn
R
Hargraves
Lukasz A
LA
Kurgan
Krzysztof J
KJ
Cios
D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D016208:Databases, Factual; D000069550:Machine Learning; D015233:Models, Statistical