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Multi-Instance Ensemble Learning With Discriminative Bags | IEEE Journals & Magazine | IEEE Xplore

Multi-Instance Ensemble Learning With Discriminative Bags

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Abstract:

Multi-instance learning (MIL) is more general and challenging than traditional supervised learning in that labels are given at the bag level. The popular feature mapping ...Show More

Abstract:

Multi-instance learning (MIL) is more general and challenging than traditional supervised learning in that labels are given at the bag level. The popular feature mapping approaches convert each bag into an instance in the new feature space. However, most of them hardly maintain the distinguishability of bags, and the MIL model does not support self-reinforcement. In this article, we propose the multi-instance ensemble learning with discriminative bags (ELDB) algorithm with two new techniques. The bag selection technique obtains a discriminative bag set (dBagSet) according to two parts. First, considering the space and label distribution of the data, the bag selection process is optimized through discriminative analysis to obtain the basic dBagSet. Second, with the state and action transfer strategy, a dBagSet with better distinguishability is obtained through self-reinforcement. The ensemble technique trains a series of classifiers with these dBagSets and obtains the final weighted model. The experimental results show that ELDB is superior to the state-of-the-art MIL mapping solutions.
Published in: IEEE Transactions on Systems, Man, and Cybernetics: Systems ( Volume: 52, Issue: 9, September 2022)
Page(s): 5456 - 5467
Date of Publication: 16 November 2021

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