Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group by Meng Han, Shurong Yang, Hongxin Wu, Jian Ding

“…Ensemble classification is widely used in multi-label algorithms, and it can be divided into homogeneous ensembles and heterogeneous ensembles according to classifier types. …”
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A novel multi-label classification algorithm based on -nearest neighbor and random walk by Zhen-Wu Wang, Si-Kai Wang, Ben-Ting Wan, William Wei Song

“…One challenge of using the random walk-based multi-label classification algorithms is to construct a random walk graph for the multi-label classification algorithms, which may lead to poor classification quality and high algorithm complexity. …”
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Multi-label Text Classification by Fusing Pseudo-label Generation and Data Augmentation by WANG Shuitao, ZHANG Sijia, SUN Xufei, YANG Jingjie, BI Tiantian, ZHANG Zhenglong

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Performance improvement of extreme multi-label classification using K-way tree construction with parallel clustering algorithm by Purvi Prajapati, Amit Thakkar

“…eXtreme Multi-Label Classification (XMLC) is the particular case of Multi-Label Classification, which deals with an extremely high number of labels. …”
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Multi-label Classification Based on Label-Aware Variational Autoencoder by SUN Hongjian, XU Pengyu, LIU Bing, JING Liping, YU Jian

“…Experimental results obtained on datasets from four different domains show that the proposed method can effectively enhance feature and label embedding and fully capture the higher-order correlation information between labels for multi-label classification tasks, and the significant superiority of the proposed method in performance is verified through a comparative analysis with state-of-the-art algorithms in terms of multiple evaluation metrics.…”
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The design of copper flotation process based on multi-label classification and regression by Haipei Dong, Fuli Wang, Dakuo He, Yan Liu

“…The copper flotation backbone process design was transformed into multi-label classification, and it was found that applying label correlation and domain knowledge to multi-label classification could significantly improve the precision of backbone process design. …”
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Multi-label software requirement smells classification using deep learning by Ashagrew Liyih Alem, Ketema Keflie Gebretsadik, Shegaw Anagaw Mengistie, Muluye Fentie Admas

“…Therefore, this study explores a deep learning-based approach to multi-label classification of software requirement smells, incorporating advanced neural network architectures such as LSTM, Bi-LSTM, and GRU with combined word embedding like ELMo and Word2Vec. …”
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Prediction model for psychological disorders in ankylosing spondylitis patients based on multi-label classification by Kun Yang, Yifan Gong, Xiaohan Xu, Tiantian Sun, Xinning Qu, Xiaxiu He, Hongxiao Liu

“…ObjectiveThis study aims to develop a predictive model to assess the likelihood of psychological disorders in patients with ankylosing spondylitis (AS) and to explore the relationships between different factors and psychological disorders.MethodsPatients were randomly divided into training and test sets in an 8:2 ratio. The Boruta algorithm was applied to select predictive factors, and a multi-label classification learning algorithm based on association rules (AR) was developed. …”
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A multi-label classification method for disposing incomplete labeled data and label relevance by Lina ZHANG, Lingpeng DAI, Tai KUANG

“…Multi-label classification methods have been applied in many real-world fields，in which the labels may have strong relevance and some of them even are incomplete or missing.However，existing multi-label classification algorithms are unable to handle both issues simultaneously.A new probabilistic model that can automatically learn and exploit multi-label relevance was proposed on label relevance and missing label classification simultaneously.By integrating out the missing information，it also provides a disciplined approach to handle missing labels.Experiments on a number of real world data sets with both complete and incomplete labels demonstrated that the proposed method can achieve higher classification and prediction evaluation scores than the existing multi-label classification algorithms.…”
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Multi-label remote sensing classification with self-supervised gated multi-modal transformers by Na Liu, Ye Yuan, Guodong Wu, Sai Zhang, Jie Leng, Lihong Wan

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Embedded feature fusion for multi-label criteria selection via local search strategy and particle swarm optimization by Suhua Chen, Xu Fang, Feng Zhai, Li Wang, Lin Lv

Subjects: “…Multi-label classification…”
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Machine learning of automatic hierarchical multi-label classification method for identifying metal failure mechanisms by Ruitong Han, Chang-Bo Liu, Wanting Sun, Shuai Yu, Haoran Zheng, Lin Deng

“…Abstract In this study, a hierarchical multi-label classification method called HFFNet-2d is proposed for the automatic classification of scanning electron microscope (SEM) images of metal failure. …”
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Multi-label classification for image tamper detection based on Swin-T segmentation network in the spatial domain by Li Li, Kejia Zhang, Jianfeng Lu, Shanqing Zhang

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Dual Passive-Aggressive Stacking k-Nearest Neighbors for Class-Incremental Multi-Label Stream Classification by Hann Hsen Tan, Chu Kiong Loo, Chaw Seng Woo

“…Class-incremental multi-label stream classification (class-incremental MLSC) requires learning algorithms to adapt to concept drifts, perform single-pass online learning, and handle emerging new labels in the data stream. …”
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Risk Measurement of TAVR Surgical Complications Based on Unbalanced Multilabel Classification Approaches by Yue Zhang, Yuantao Xie

“…In the empirical analysis, with real surgical data from a hospital in the United States, a combination of multi-label random sampling and representative multi-label classification algorithms was used to fit the data. …”
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A Hubness Information-Based k-Nearest Neighbor Approach for Multi-Label Learning by Zeyu Teng, Shanshan Tang, Min Huang, Xingwei Wang

“…Multi-label classification (MLC) plays a crucial role in various real-world scenarios. …”
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Label dependency modeling in Multi-Label Naïve Bayes through input space expansion by PKA Chitra, Saravana Balaji Balasubramanian, Omar Khattab, Mhd Omar Al-Kadri

“…Multi-label techniques often employ a similar feature space to build classification models for every label. …”
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A comprehensive transplanting of black-box adversarial attacks from multi-class to multi-label models by Zhijian Chen, Qi Zhou, Yujiang Liu, Wenjian Luo

“…Therefore, existing multi-class attack algorithms cannot directly attack multi-label classification models. …”
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Customer service complaint work order classification based on matrix factorization and attention multi-task learning by Yong SONG, Zhiwei YAN, Yukun QIN, Dongming ZHAO, Xiaozhou YE, Yuanyuan CHAI, Ye OUYANG

“…The automatic classification of complaint work orders is the requirement of the digital and intelligent development of customer service of communication operators.The categories of customer service complaint work orders have multiple levels, each level has multiple labels, and the levels are related, which belongs to a typical hierarchical multi-label text classification (HMTC) problem.Most of the existing solutions are based on classifiers to process all classification labels at the same time, or use multiple classifiers for each level, ignoring the dependence between hierarchies.A matrix factorization and attention-based multi-task learning approach (MF-AMLA) to deal with hierarchical multi-label text classification tasks was proposed.Under the classification data of real complaint work orders in the customer service scenario of communication operators, the maximum Top1 F1 value of MF-AMLA is increased by 21.1% and 5.7% respectively compared with the commonly used machine learning algorithm and deep learning algorithm in this scenario.It has been launched in the customer service system of one mobile operator, the accuracy of model output is more than 97%, and the processing efficiency of customer service agent unit time has been improved by 22.1%.…”
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High-Performance Deployment Operational Data Analytics of Pre-Trained Multi-Label Classification Architectures with Differential-Evolution-Based Hyperparameter Optimization (AutoDE... by Teo Prica, Aleš Zamuda

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