Abstract
This paper introduces two approaches for solving Multiple Instance Problems (MIP) in which the traditional instance localization assumption is not met. We introduce a technique which transforms individual feature values in the attempt to align the data to the MIP localization assumption and a new MIP learning algorithm which identifies a region enclosing the majority (negative) class while excluding at least one instance from each positive (minority class) bag. The proposed methods are evaluated on synthetic datasets, as well as on a real-world manufacturing defect identification dataset. The real-world dataset poses additional challenges: data with noise, large imbalance and overlap.
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Graur, DO., Mariş, RA., Potolea, R., Dînşoreanu, M., Lemnaru, C. (2018). Complex Localization in the Multiple Instance Learning Context. In: Appice, A., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2017. Lecture Notes in Computer Science(), vol 10785. Springer, Cham. https://doi.org/10.1007/978-3-319-78680-3_7
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DOI: https://doi.org/10.1007/978-3-319-78680-3_7
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