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Abstract:
We present a new multiple-instance (MI) learning technique
(EMDD) that combines EM with the diverse density (DD) algorithm.
EM-DD is a general-purpose MI algorithm that can be applied with
boolean or real-value labels and makes real-value predictions. On
the boolean Musk benchmarks, the EM-DD algorithm without any
tuning significantly outperforms all previous algorithms. EM-DD
is relatively insensitive to the number of relevant attributes in
the data set and scales up well to large bag sizes. Furthermore,
EMDD provides a new framework for MI learning, in which the MI
problem is converted to a single-instance setting by using EM to
estimate the instance responsible for the label of the bag.
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