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构建新包空间的多示例学习方法 被引量：1

Multiple Instance Learning Method Based on Building New Bag Space
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摘要 针对已有神经网络方法采用示例决定标记从而导致多示例学习(MIL)中包结构信息丢失的问题,提出了一种新的RK_BP多示例学习方法.在示例空间,首先采用粗糙集对其进行属性约简;然后进行K均值聚类,利用聚类点构造新包空间;在新空间中,利用误差反向传播神经网络算法进行分类.在多个测试数据集上对算法进行测试,结果表明该算法可有效解决已有神经网络方法包结构信息丢失问题,明显提高分类性能. Aiming at bag structure information loss problem caused by single instance deciding bag label in multiple instance learning （MIL）, a new MIL algorithm named RK_BP is proposed. Firstly, rough set method is adopted to reduce the redundant information in the instance feature space, then K means algorithm is applied to cluster and build a new bag space, and finally back propagation algorithm is used to classify bags in the new space. Experiments on data sets show that this algorithm deals well with the multiple instance problems and provides better classification results.
作者 温超 耿国华 李展
机构地区 西北大学信息科学与技术学院
出处 《西安交通大学学报》 EI CAS CSCD 北大核心 2011年第8期62-66,117,共6页 Journal of Xi'an Jiaotong University
基金 国家自然科学基金资助项目(60873094) 高等学校博士学科点专项科研基金资助项目(200806970014)
关键词 多示例学习 反向传播算法 粗糙集 K均值聚类 新空间 multiple instance learning back propagation rough set K means clustering newspace
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献17

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二级参考文献31

共引文献306

同被引文献5

  • 1Dietterich T G,Lathrop R H,Lozano-P'erez T.Solving the multiple instance problem with axisparallel rectangles[J].Artificial Intelligence,1997,89(1-2):31-71.
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  • 3Gary Doran,Soumya Ray,SMILe.Shuffled Multiple-Instance Learning[C] // Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence,2013.
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  • 5Zhou Z,Sun Y,Li Y.Multi-instance learning by treating instances as non-IID samples[C] // In Proceedings of the 26th International Conference on Machine Learning,2009.1249-1256.

引证文献1

西安交通大学学报
2011年 第8期

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