Multiple Instance Learning Method Based on Building New Bag Space

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Multiple Instance Learning Method Based on Building New Bag Space

更新时间：2025-07-09

- Multiple Instance Learning Method Based on Building New Bag Space
- Vol. 45, Issue 8, Pages: 62-66+117(2011)
- 作者机构：
  
  西北大学信息科学与技术学院,西安,710069
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Online First：10 August 2011，
  
  Published：2011
- 稿件说明：
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Multiple Instance Learning Method Based on Building New Bag Space[J]. 2011, 45(8): 62-66+117.
DOI：

Multiple Instance Learning Method Based on Building New Bag Space[J]. 2011, 45(8): 62-66+117. DOI：

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.

Abstract

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.

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references

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