TY - JOUR
AU - D.T. Nguyen
AU - C.D. Nguyen
AU - R. Hargraves
AU - L.A. Kurgan
AU - Krzysztof Cios
AB - 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 algorithms, 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.
BT - IEEE Transactions on Cybernetics
LA - eng
M1 - 1
N2 - 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 algorithms, 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.
PY - 2013
EP - 143–154
T2 - IEEE Transactions on Cybernetics
TI - mi-DS: Multiple-instance learning algorithm
VL - 43
SN - 2168-2267
ER -