| 标题 |
 A novel granular ball computing-based fuzzy rough set for feature selection in label distribution learning 基于粒度球计算的模糊粗糙集在标签分布学习中的特征选择
相关领域
人工智能
特征选择
粒度计算
计算机科学
模式识别（心理学）
粒度
判别式
特征向量
维数之咒
机器学习
数据挖掘
粗集
数学
操作系统
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| 网址 | |
| DOI | |
| 其它 | Label distribution learning is a widely studied supervised learning diagram that can handle the problem of label ambiguity. The increasing size of datasets is accompanied by the disaster of dimensionality, which implies that the arrival of redundant and noisy features undermines the effect of label distribution learning. As a crucial data-preprocessing technique, feature selection is capable of choosing discriminative features. However, due to the complex issue of label ambiguity, traditional feature selection approaches for datasets with logical labels cannot be applied to label distribution data. |
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