Solving the multiple instance problem with axis-parallel rectangles
Solving the multiple instance problem with axis-parallel rectangles
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DOI：
10.1016/s0004-3702(96)00034-3
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发表时间：
1997-01-01
影响因子：
14.4
通讯作者：
LozanoPerez, T
中科院分区：
文献类型：
--
作者：
Dietterich, TG;Lathrop, RH;LozanoPerez, T
多个实例问题出现在训练示例模棱两可的任务中：一个示例对象可能具有描述它的许多替代特征向量（实例），但这些特征向量中只有一个可能负责观察到的对象的分类。本文描述并比较了三种算法，这些算法学习轴平行的矩形来解决多个实例问题。忽略多个实例问题的算法表现差。直接面对多个实例问题的算法（通过尝试识别哪些特征向量负责观察到的分类）的算法表现最好，从而在Musk气味预测任务上提供了89％的正确预测。本文还说明了使用人工数据来调试和比较这些算法。
The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms.