Multiple-instance learning with pairwise instance similarity
Liming Yuan ; Jiafeng Liu ; Xianglong Tang
International Journal of Applied Mathematics and Computer Science, Tome 24 (2014), p. 567-577 / Harvested from The Polish Digital Mathematics Library

Multiple-Instance Learning (MIL) has attracted much attention of the machine learning community in recent years and many real-world applications have been successfully formulated as MIL problems. Over the past few years, several Instance Selection-based MIL (ISMIL) algorithms have been presented by using the concept of the embedding space. Although they delivered very promising performance, they often require long computation times for instance selection, leading to a low efficiency of the whole learning process. In this paper, we propose a simple and efficient ISMIL algorithm based on the similarity of pairwise instances within a bag. The basic idea is selecting from every training bag a pair of the most similar instances as instance prototypes and then mapping training bags into the embedding space that is constructed from all the instance prototypes. Thus, the MIL problem can be solved with the standard supervised learning techniques, such as support vector machines. Experiments show that the proposed algorithm is more efficient than its competitors and highly comparable with them in terms of classification accuracy. Moreover, the testing of noise sensitivity demonstrates that our MIL algorithm is very robust to labeling noise.

Publié le : 2014-01-01
EUDML-ID : urn:eudml:doc:271918
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     author = {Liming Yuan and Jiafeng Liu and Xianglong Tang},
     title = {Multiple-instance learning with pairwise instance similarity},
     journal = {International Journal of Applied Mathematics and Computer Science},
     volume = {24},
     year = {2014},
     pages = {567-577},
     zbl = {1322.68163},
     language = {en},
     url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-amcv24i3p567bwm}
}
Liming Yuan; Jiafeng Liu; Xianglong Tang. Multiple-instance learning with pairwise instance similarity. International Journal of Applied Mathematics and Computer Science, Tome 24 (2014) pp. 567-577. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-amcv24i3p567bwm/

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