计算机科学 ›› 2022, Vol. 49 ›› Issue (11): 98-108.doi: 10.11896/jsjkx.210900076
闫振超, 舒文豪, 谢昕
YAN Zhen-chao, SHU Wen-hao, XIE Xin
摘要： 许多实际应用中的数据集是由符号型、数值型和缺失型特征构成的混合数据。针对混合数据的决策标记,由于获取全部数据的决策标记需要耗费大量的人工和时间成本,只能为部分数据进行决策标记,因此产生了部分标记数据。同时,现实应用领域中数据是动态产生的,即数据维度随着不同的需求动态地增加或删减。针对混合数据的高维性、部分标记和动态性,文中提出了两种面向部分标记混合数据的增量式特征选择算法。首先,利用信息粒度对部分标记混合数据的特征进行重要度分析;其次,当特征集发生动态变化时,结合增量学习的思想,给出信息粒度的增量更新机制;然后,在此基础上提出了两种面向部分标记混合数据的增量式特征选择算法;最后,通过与其他算法在UCI数据集上的实验结果进行对比,进一步验证了所提算法的可行性和有效性。
中图分类号:
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