Abstract
This paper introduced a probabilistic approach to the multiple-instance learning (MIL) problem with two Bayes classification algorithms. The first algorithm, named Instance-Vote, provides a simple approach for posterior probability estimation. The second algorithm, Embedded Kernel Density Estimation (EKDE), enables data visualization during classification. Both algorithms were evaluated using MUSK benchmark data sets and the results are highly competitive with existing methods.
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This work was supported by the Department of Computer and Information Science, University of Mississippi.
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Zhang, S., Chen, Y., Wilkins, D. (2017). A Probabilistic Approach to Multiple-Instance Learning. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_30
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DOI: https://doi.org/10.1007/978-3-319-59575-7_30
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