Results 41 to 50 of about 7,571,754 (304)
A New Ant Colony Algorithm for Multi-Label Classification with Applications in Bioinformatics [PDF]
, 2006 The conventional classification task of data mining can be called single-label classification, since there is a single class attribute to be predicted.
Chan, Allen +3 more
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Groupwise Ranking Loss for Multi-Label Learning
IEEE Access, 2020 This work studies multi-label learning (MLL), where each instance is associated with a subset of positive labels. For each instance, a good multi-label predictor should encourage the predicted positive labels to be close to its ground-truth positive ones.
Yanbo Fan +5 more
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Representation Learning With Dual Autoencoder for Multi-Label Classification
IEEE Access, 2021 Multi-label classification aims to deal with the problem that an object may be associated with one or more labels, which is a more difficult task due to the complex nature of multi-label data. The crucial problem of multi-label classification is the more
Yi Zhu +5 more
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Multi-Label Learning via Feature and Label Space Dimension Reduction
IEEE Access, 2020 In multi-label learning, each object belongs to multiple class labels simultaneously. In the data explosion age, the size of data is often huge, i.e., large number of instances, features and class labels.
Jun Huang +4 more
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Deep Learning for Multi-label Classification
CoRR, 2014 In multi-label classification, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classification time. Developing better feature-space representations has been predominantly employed to reduce complexity, e.g., by eliminating non-helpful feature attributes from the input ...
Jesse Read, Fernando Pérez-Cruz
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A Survey on Extreme Multi-label Learning
CoRR, 2022 Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good performance in various tasks, they implicitly assume the size of target label space is not huge, which can be restrictive for real-world scenarios.
Tong Wei 0001 +4 more
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Learning in multi-agent systems [PDF]
, 2001 In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as ...
Alonso, E. +14 more
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Optimized Ranking Algorithm Based on Margin Criterion for Multi-Label Learning [PDF]
Jisuanji gongcheng, 2020 For classification problems in multi-label learning,the algorithm adaptation methods that transform them into a ranking problem and rank the output labels according to their relevance to the examples have made great success.This paper proposes a multi ...
JIN Yazhou, ZHANG Zhengjun, YAN Zihan, WANG Yaping
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The human gut microbiome across the life course
FEBS Letters, EarlyView.Despite significant individual variation and continuous change throughout life, the human gut microbiome follows some life stage‐specific trends. This article provides a brief overview of how gut microbiome composition shifts across different phases of life. Created in BioRender. Özkurt, E. (2026) https://BioRender.com/8q4nrnc.
Alise J. Ponsero +4 more
wiley +1 more source
A new genetic algorithm for multi-label correlation-based feature selection. [PDF]
, 2015 This paper proposes a new Genetic Algorithm for Multi-Label Correlation-Based Feature Selection (GA-ML-CFS). This GA performs a global search in the space of candidate feature subset, in order to select a high-quality feature subset is used by a multi ...
Jungjit, Suwimol, Freitas, Alex A.
core +1 more source