2019
DOI: 10.1007/s10994-018-5773-6
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Dynamic principal projection for cost-sensitive online multi-label classification

Abstract: We study multi-label classification (MLC) with three important real-world issues: online updating, label space dimension 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 foundation of CS-DPP is an online LSDR framework derived from a leading LSDR algorithm. In particular, CS-DPP is equipped with a… Show more

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Cited by 5 publications
(3 citation statements)
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“…According to the profit maximization goal of the APP platform, the input layer and price-related factors are sorted by importance [41][42][43]; it can be denoted by the following formula:…”
Section: Research On English Film and Television Resource Informationmentioning
confidence: 99%
“…According to the profit maximization goal of the APP platform, the input layer and price-related factors are sorted by importance [41][42][43]; it can be denoted by the following formula:…”
Section: Research On English Film and Television Resource Informationmentioning
confidence: 99%
“…[12] proposed an extreme learning machine-based online universal classifier that is independent of classification type and can perform all three types of classification. Moreover, cost-sensitive dynamic principal projection (CS-DPP) [13] resolves three important real-world issues: online updating, label space dimension reduction (LSDR), and cost sensitivity. Based on binary relevance, Refs.…”
Section: Related Work 1online Learningmentioning
confidence: 99%
“…Extended author information available on the last page of the article label ambiguity, where each instance can be associated with multiple possible class labels simultaneously. Many well-established approaches have been proposed, such as (Chu et al 2019;Decubber et al 2019;Liu 2019;Liu and Shen 2019;Masera and Blanzieri 2019;Nguyen and Hüllermeier 2019;Park and Read 2019;Huang et al 2018;Wydmuch et al 2018;Zhang and Wu 2019). In multi-label learning, a common assumption is that all the class labels and their values are observed before the training process.…”
Section: Background and Motivationmentioning
confidence: 99%