  In this paper, we investigate the multi-variate sequence classification
problem from a multi-instance learning perspective. Real-world sequential data
commonly show discriminative patterns only at specific time periods. For
instance, we can identify a cropland during its growing season, but it looks
similar to a barren land after harvest or before planting. Besides, even within
the same class, the discriminative patterns can appear in different periods of
sequential data. Due to such property, these discriminative patterns are also
referred to as shifting patterns. The shifting patterns in sequential data
severely degrade the performance of traditional classification methods without
sufficient training data.
  We propose a novel sequence classification method by automatically mining
shifting patterns from multi-variate sequence. The method employs a
multi-instance learning approach to detect shifting patterns while also
modeling temporal relationships within each multi-instance bag by an LSTM model
to further improve the classification performance. We extensively evaluate our
method on two real-world applications - cropland mapping and affective state
recognition. The experiments demonstrate the superiority of our proposed method
in sequence classification performance and in detecting discriminative shifting
patterns.
