Captured multi-label relations via joint deep supervised autoencoder

Lian, Si-ming; Liu, Jianwei; Lu, Run-kun; Luo, Xianglong

doi:10.1016/j.asoc.2018.10.035

Cited by 12 publications

(3 citation statements)

References 43 publications

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“…According to the structure of data, machine learning is divided into supervised, unsupervised, semi-supervised and reinforcement learning (Zhang et al 2022d;Tran and Ha 2022). The characteristic of supervised learning is that training data has corresponded labels (Lian et al 2019;Zhang et al 2018). According to the learning strategy, supervised learning is divided into generative method and discriminant method.…”

Section: General Concepts Of Machine Learningmentioning

confidence: 99%

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Zhang

Chen

et al. 2023

Biochar

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Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enumeration is powerless. In recent years, machine learning has been gradually employed for biochar, but there is no comprehensive review on the whole process regulation of biochar adsorbents, covering synthesis optimization and adsorption modeling. This review article systematically summarized the application of machine learning in biochar adsorbents from the perspective of all-round regulation for the first time, including the synthesis optimization and adsorption modeling of biochar adsorbents. Firstly, the overview of machine learning was introduced. Then, the latest advances of machine learning in biochar synthesis for pollutant removal were summarized, including prediction of biochar yield and physicochemical properties, optimal synthetic conditions and economic cost. And the application of machine learning in pollutant adsorption by biochar was reviewed, covering prediction of adsorption efficiency, optimization of experimental conditions and revelation of adsorption mechanism. General guidelines for the application of machine learning in whole-process optimization of biochar from synthesis to adsorption were presented. Finally, the existing problems and future perspectives of machine learning for biochar adsorbents were put forward. We hope that this review can promote the integration of machine learning and biochar, and thus light up the industrialization of biochar. Graphical Abstract

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Section: General Concepts Of Machine Learningmentioning

confidence: 99%

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Zhang

Chen

et al. 2023

Biochar

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“…Joint binary cross-entropy (JBCE) loss [ 21 ] is proposed to train the joint binary neural network (JBNN) to capture label relations. To reduce the computational complexity, partial label dependence can also contribute to this task, which is demonstrated in [ 22 ]. A semisupervised multilabel method is proposed in [ 23 ], while label correlations are incorporated by modifying the loss function.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Network with Label Embedding for Contextual Emotion Recognition

Deng

Ren

2021

Research

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Emotion recognition has been used widely in various applications such as mental health monitoring and emotional management. Usually, emotion recognition is regarded as a text classification task. Emotion recognition is a more complex problem, and the relations of emotions expressed in a text are nonnegligible. In this paper, a hierarchical model with label embedding is proposed for contextual emotion recognition. Especially, a hierarchical model is utilized to learn the emotional representation of a given sentence based on its contextual information. To give emotion correlation-based recognition, a label embedding matrix is trained by joint learning, which contributes to the final prediction. Comparison experiments are conducted on Chinese emotional corpus RenCECps, and the experimental results indicate that our approach has a satisfying performance in textual emotion recognition task.

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“…Several authors [46,20,38] have proposed multi-label classification solutions inspired on deep learning techniques. Some of these solutions [4,17] rely on the autoencoders [12,37], which allow for the unsupervised learning of features.…”

Section: Introductionmentioning

confidence: 99%