@article{bibcite_14580,
  author = {D.T. Nguyen and C.D. Nguyen and R. Hargraves and L.A. Kurgan and Krzysztof Cios},
  title = {mi-DS: Multiple-instance learning algorithm},
  abstract = {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.},
  year = {2013},
  journal = {IEEE Transactions on Cybernetics},
  volume = {43},
  number = {1},
  pages = {143{\textendash}154},
  issn = {2168-2267},
  language = {eng},
}