Results 11 to 20 of about 4,317,504 (301)
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management, 2005 Common approaches to multi-label classification learn independent classifiers for each category, and employ ranking or thresholding schemes for classification. Because they do not exploit dependencies between labels, such techniques are only well-suited to problems in which categories are independent.
Ghamrawi, Nadia, McCallum, Andrew
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Ensemble methods for multi-label classification [PDF]
Expert Systems with Applications, 2014 Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper
Lior Rokach, Alon Schclar, Ehud Itach
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MULTI-LABEL RANKING METHOD BASED ON POSITIVE CLASS CORRELATIONS
Jordanian Journal of Computers and Information Technology, 2020 Multi-label classification is a general type of classification that has attracted many researchers in the last two decades due to its applicability to many modern domains, such as scene classification, bioinformatics and text classification, among others.
Raed Alazaidah +3 more
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Automatic multi-label subject indexing in a multilingual environment [PDF]
, 2003 This paper presents an approach to automatically subject index fulltext documents with multiple labels based on binary support vector machines(SVM). The aim was to test the applicability of SVMs with a real world dataset.
Lauser, Boris, Hotho, Andreas
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Multi-Label ECG Signal Classification Based on Ensemble Classifier
IEEE Access, 2020 Electrocardiogram (ECG) has been proved to be the most common and effective approach to investigate the cardiovascular disease because that it is simple, non-invasive and low cost.
Zhanquan Sun +3 more
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Harnessing Multi-label Classification Approaches for Economic Phenomena Categorization
ASEAN Journal on Science and Technology for Development, 2021 One fashion to report a country’s economic state is by compiling economic phenomena from several sources. The collected data may be explored based on their sentiments and economic categories.
Nofriani, Novianto Budi Kurniawan
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Weak Label Feature Selection Method Based on Neighborhood Rough Sets and Relief [PDF]
Jisuanji kexue, 2022 In multi-label learning and classification, existing feature selection algorithms based on neighborhood rough sets will use classification margin of samples as the neighborhood radius.However, when the margin is too large, the classification may be ...
SUN Lin, HUANG Miao-miao, XU Jiu-cheng
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Multi-label Classification with Meta-Labels [PDF]
2014 IEEE International Conference on Data Mining, 2014 The area of multi-label classification has rapidly developed in recent years. It has become widely known that the baseline binary relevance approach can easily be outperformed by methods which learn labels together. A number of methods have grown around the label power set approach, which models label combinations together as class values in a multi ...
Jesse Read, Antti Puurula, Albert Bifet
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Uncertainty in Extreme Multi-label Classification
CoRR, 2022 Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain has only focused on problems with small label spaces and ignored eXtreme Multi-label Classification (XMC), which is an essential task in the era of big data for web-scale ...
Jyun-Yu Jiang +4 more
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Multi-Label Classification Algorithm Based on Embedded Feature Extraction [PDF]
Jisuanji gongcheng, 2019 Dimensionality reduction and feature selection methods based on single-label classification cannot be directly applied to multi-label learning.If a multi-label learning problem is composed into multiple independent single-label learning problems to ...
WANG Xiaoying, XIE Jun, TAO Xingliu, SHAO Dongsheng, WANG Zhong
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