Multi-Label Learning via Feature and Label Space Dimension Reduction
نویسندگان
چکیده
منابع مشابه
Feature-aware Label Space Dimension Reduction for Multi-label Classification
Label space dimension reduction (LSDR) is an efficient and effective paradigm for multi-label classification with many classes. Existing approaches to LSDR, such as compressive sensing and principal label space transformation, exploit only the label part of the dataset, but not the feature part. In this paper, we propose a novel approach to LSDR that considers both the label and the feature par...
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To tackle a multi-label classification problem with many classes, recently label space dimension reduction (LSDR) is proposed. It encodes the original label space to a low-dimensional latent space and uses a decoding process for recovery. In this paper, we propose a novel method termed FaIE to perform LSDR via Feature-aware Implicit label space Encoding. Unlike most previous work, the proposed ...
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Multi-label classification (MLC) extends multi-class classification by tagging each instance as multiple classes simultaneously. Different real-world MLC applications often demand different evaluation criteria (costs), which calls for cost-sensitive MLC (CSMLC) algorithms that can easily take the criterion of interest into account. Nevertheless, existing CSMLC algorithms generally suffer from h...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2969238