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A heuristic approach for multiple instance learning by linear separation
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DOI：10.1007/s00500-021-06713-1.png)
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Abstract
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We present a fast heuristic approach for solving a binary multiple instance learning (MIL) problem, which consists in discriminating between two kinds of item sets: the sets are called bags and the items inside them are called instances. Assuming that only two classes of instances are allowed, a common standard hypothesis states that a bag is positive if it contains at least a positive instance and it is negative when all its instances are negative. Our approach constructs a MIL separating hyperplane by preliminary fixing the normal and reducing the learning phase to a univariate nonsmooth optimization problem, which can be quickly solved by simply exploring the kink points. Numerical results are presented on a set of test problems drawn from the literature.
Keywords:
Multiple instance learning (MIL)
Linear separation
Nonsmooth optimization
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SOFT COMPUTING
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