Dynamic principal projection for cost-sensitive online multi-label classification
نویسندگان
چکیده
منابع مشابه
Dynamic Principal Projection for Cost-Sensitive Online Multi-Label Classification
We study multi-label classification (MLC) with three important real-world issues: online updating, label space dimensional reduction (LSDR), and cost-sensitivity. Current MLC algorithms have not been designed to address these three issues simultaneously. In this paper, we propose a novel algorithm, cost-sensitive dynamic principal projection (CS-DPP) that resolves all three issues. The foundati...
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Proof. The proof is similar to the one in (Beygelzimer et al., 2008), which is based on defining the overallregret of any subtree. The key change in our proof is to define the path-regret of any subtree to be the total regret of the nodes on the ideal path of the subtree. The induction step follows similarly from the proof in (Beygelzimer et al., 2008) by considering two cases: one for the idea...
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Proof. The proof is similar to the one in (Beygelzimer et al., 2008), which is based on defining the overall-regret of any subtree. The key change in our proof is to define the path-regret of any subtree to be the total regret of the nodes on the ideal path of the subtree. The induction step follows similarly from the proof in (Beygelzimer et al., 2008) by considering two cases: one for the ide...
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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: Machine Learning
سال: 2019
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-018-5773-6