Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers
This work addresses the limitation of weak classifiers in MIL for applications like time-series classification, though it appears incremental as it builds on existing shapelet-based methods with theoretical improvements.
The paper tackles the problem of Multiple-Instance Learning (MIL) by proposing a new formulation that uses infinitely many shapelets to create stronger classifiers, with empirical results showing effectiveness for MIL and time-series classification tasks.
We propose a new formulation of Multiple-Instance Learning (MIL). In typical MIL settings, a unit of data is given as a set of instances called a bag and the goal is to find a good classifier of bags based on similarity from a single or finitely many "shapelets" (or patterns), where the similarity of the bag from a shapelet is the maximum similarity of instances in the bag. Classifiers based on a single shapelet are not sufficiently strong for certain applications. Additionally, previous work with multiple shapelets has heuristically chosen some of the instances as shapelets with no theoretical guarantee of its generalization ability. Our formulation provides a richer class of the final classifiers based on infinitely many shapelets. We provide an efficient algorithm for the new formulation, in addition to generalization bound. Our empirical study demonstrates that our approach is effective not only for MIL tasks but also for Shapelet Learning for time-series classification.