Constructing target concept in multiple instance learning using maximum partial entropy
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
An efficient parallel neural network-based multi-instance learning algorithm
The Journal of Supercomputing
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This paper proposes a new multiple instance learning (MIL) method based on a MIL back-propagation neural network (MIBP), which is an extension of the standard back-propagation neural network (BPNN) that uses labeled bags of instances as training data. The method finds a concept point t in the feature space which is close to instances from positive bags and far from instances in negative bags. Our method is as follows: First, train MIBP with positive and negative bags. Second, extract t from the trained MIBP. This is achieved by, for each positive bag, presenting all the instances to the trained MIBP and selecting the one with maximal output value. The t is then obtained by averaging all the extracted instances. Finally, a sensitivity analysis of the trained MIBP is performed to obtain feature relevance/weighting information. We conducted experiments to measure the performance of the obtained t when used for classification purposes. The experimental results on the musk data set and a subset of the Corel image data set show that our method has better classification performance and is more computationally efficient than other well-established MIL methods.