Abstract
Multiple Instance Learning is concerned with learning from sets (bags) of feature vectors (instances), where the bags are labeled, but the instances are not. One of the ways to classify bags is using a (dis)similarity space, where each bag is represented by its dissimilarities to certain prototypes, such as bags or instances from the training set. The instance-based representation preserves the most information, but is very high-dimensional, whereas the bag-based representation has lower dimensionality, but risks throwing away important information. We show a connection between these representations and propose an alternative representation based on combining classifiers, which can potentially combine the advantages of the other methods. The performances of the ensemble classifiers are disappointing, but require further investigation. The bag-based representation preserves sufficient information to classify bags correctly and produces the best results on several datasets.
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Cheplygina, V., Tax, D.M.J., Loog, M. (2013). Combining Instance Information to Classify Bags. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_2
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DOI: https://doi.org/10.1007/978-3-642-38067-9_2
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