多示例学习的可行域定位及快速因果实例选择

Feasible region localization and fast causal instance selection for multi-instance learning

提出了一种多示例学习的可行域定位及快速因果实例选择(feasible region localization and fast causal instance selection for multi-instance learning,FFCM)算法,包含3个技术。可行域定位技术基于距离度量,从正包中选出具有代表性的实例作为候选实例;然后利用概率分析筛选负裁判包,以最大限度缩减选择因果实例的可行域范围。快速因果实例选择技术利用候选实例与负裁判包的因果关系构建融合包,设计因果性评判指标,使用先验知识从候选实例中选择出因果实例。包映射技术基于因果实例和差值映射函数,将包映射为有较高可区分性的单向量。本算法在27个常用数据集上进行了实验,并与6个前沿的MIL算法进行了对比,实验结果展示了FFCM的良好分类性能。

This paper proposes a feasible region localization and fast causal instance selection(FFCM)algorithm for multi-instance learning,incorporating three techniques.To minimize the feasible region of data,the fast feasible region localization technique is used to select representative instances from the positive bags as candidate instances based on distance measurement,and reduces the negative referee bags through probability analysis.The fast causal instance-based selection technique uses the causal relationship between candidate instances and negative referee bags to construct fusion bags.Subsequently,prior knowledge is employed to select causal instances from candidate instances based on the designed causal instance criteria.The bag mapping technique maps bags into single vectors with high distinguishability using causal instances and a difference-based mapping function.The proposed algorithm is compared with 6 state-of-the-art MIL algorithms on 27 commonly used datasets.The experimental results show that the proposed FFCM exhibits comparable classification performance.