Theory and Algorithms for Shapelet-Based Multiple-Instance Learning
Theory and Algorithms for Shapelet-Based Multiple-Instance Learning
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DOI：
10.1162/neco_a_01297
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发表时间：
2020-05
影响因子：
2.9
通讯作者：
D. Suehiro;Kohei Hatano;Eiji Takimoto;Shuji Yamamoto;Kenichi Bannai;A. Takeda
中科院分区：
文献类型：
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作者：
D. Suehiro;Kohei Hatano;Eiji Takimoto;Shuji Yamamoto;Kenichi Bannai;A. Takeda
我们提出了一种新的多实例学习（MIL）公式，其中一个数据单元由一组称为袋的实例组成。目标是基于与“shapelet”（或模式）的相似性找到一个好的袋子分类器，其中袋子与shapelet的相似性是袋子中实例的最大相似性。在以前的工作中，一些训练实例被选择为shapelets，没有理论依据。在我们的公式中，我们使用了所有可能的，也就是无限多的shapelets，从而产生了更丰富的分类器类。我们证明了该公式是可处理的，即它可以通过线性规划提升（LPBoost）简化为有限（实际上是多项式）大小的凸（DC）规划的差异。我们的理论结果也证明了一些前人工作的启发式。该算法的时间复杂度高度依赖于训练样本中所有实例集合的大小。为了适用于包含大量实例的数据，我们还提出了一种算法的启发式选择，而不会失去理论保证。我们的实证研究表明，我们的算法一致地适用于时间序列分类和各种MIL任务的shapelet学习任务，并且与现有方法具有相当的精度。此外，我们还表明，所提出的启发式算法允许我们在合理的计算时间内获得结果。
We propose a new formulation of multiple-instance learning (MIL), in which a unit of data consists of a set of instances called a bag. The goal is to find a good classifier of bags based on the similarity with a “shapelet” (or pattern), where the similarity of a bag with a shapelet is the maximum similarity of instances in the bag. In previous work, some of the training instances have been chosen as shapelets with no theoretical justification. In our formulation, we use all possible, and thus infinitely many, shapelets, resulting in a richer class of classifiers. We show that the formulation is tractable, that is, it can be reduced through linear programming boosting (LPBoost) to difference of convex (DC) programs of finite (actually polynomial) size. Our theoretical result also gives justification to the heuristics of some previous work. The time complexity of the proposed algorithm highly depends on the size of the set of all instances in the training sample. To apply to the data containing a large number of instances, we also propose a heuristic option of the algorithm without the loss of the theoretical guarantee. Our empirical study demonstrates that our algorithm uniformly works for shapelet learning tasks on time-series classification and various MIL tasks with comparable accuracy to the existing methods. Moreover, we show that the proposed heuristics allow us to achieve the result in reasonable computational time.