  Detecting anomalies over real-world datasets remains a challenging task. Data
annotation is an intensive human labor problem, particularly in sequential
datasets, where the start and end time of anomalies are not known. As a result,
data collected from sequential real-world processes can be largely unlabeled or
contain inaccurate labels. These characteristics challenge the application of
anomaly detection techniques based on supervised learning. In contrast,
Multiple Instance Learning (MIL) has been shown effective on problems with
incomplete knowledge of labels in the training dataset, mainly due to the
notion of bags. While largely under-leveraged for anomaly detection, MIL
provides an appealing formulation for anomaly detection over real-world
datasets, and it is the primary contribution of this paper. In this paper, we
propose an MIL-based formulation and various algorithmic instantiations of this
framework based on different design decisions for key components of the
framework. We evaluate the resulting algorithms over four datasets that capture
different physical processes along different modalities. The experimental
evaluation draws out several observations. The MIL-based formulation performs
no worse than single instance learning on easy to moderate datasets and
outperforms single-instance learning on more challenging datasets. Altogether,
the results show that the framework generalizes well over diverse datasets
resulting from different real-world application domains.
