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Comprehensive Comparative Study of Multi-Label Classification Methods [article]

Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski, Dragi Kocev
2021 arXiv   pre-print
Multi-label classification (MLC) has recently received increasing interest from the machine learning community.  ...  This work provides a comprehensive empirical study of a wide range of MLC methods on a plethora of datasets from various domains.  ...  CONCLUSION This works presents the most comprehensive comparative study of MLC methods to date. It presents an in-depth theoretical and empirical analysis of a variety of MLC methods.  ... 
arXiv:2102.07113v2 fatcat:jtjefamw35fetjtnatmjvjl544

Label Relation Inference for Multi-Label Aerial Image Classification

Yuansheng Hua, Lichao Mou, Xiao Xiang Zhu
2019 IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium  
Multi-label aerial image classification is a challenging visual task and obtaining increasing attention recently.  ...  Most of the existing methods resort to training independent classifier for each label, while underlying label correlations are not fully exploited while making predictions.  ...  Experiments are conducted on the UCM multi-label dataset and comparisons with relevant existing methods are performed for a comprehensive evaluation.  ... 
doi:10.1109/igarss.2019.8898934 dblp:conf/igarss/HuaMZ19 fatcat:xwo3x2rmdfabpbyyqyie6ky3ee

GML_DT: A Novel Graded Multi-label Decision Tree Classifier

Wissal Farsal, Mohammed Ramdani, Samir Anter
2021 International Journal of Advanced Computer Science and Applications  
The goal of Graded Multi-label Classification (GMLC) is to assign a degree of membership or relevance of a class label to each data point.  ...  As opposed to multi-label classification tasks which can only predict whether a class label is relevant or not.  ...  Graded Multi-label Classifiers Graded multi-label classification [1] was formalized as an extension of multi-label classification [15] [16] , to predict the degrees of relevance of the labels rather  ... 
doi:10.14569/ijacsa.2021.0121233 fatcat:7hypf6gr2fbi3c2brvtiwhvuxy

Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation [article]

Tong Shen, Guosheng Lin, Chunhua Shen, Ian Reid
2017 arXiv   pre-print
With comprehensive experiments, we show that the dense multi-label can successfully remove the implausible labels and clear the confusion so as to boost the performance of semantic segmentation systems  ...  Per-pixel semantic labelling of an image benefits greatly from the ability to consider region consistency both locally and globally.  ...  Multi-label classification has also been widely studied. Traditional methods are based on graphical models [20, 16] , while the recent studies benefit more from CNNs [13, 14, 17] . Gong et al.  ... 
arXiv:1701.07122v1 fatcat:bn74e6p3szg35b5blcsnrrlmqu

Reading Profiles in Multi-Site Data With Missingness

Mark A. Eckert, Kenneth I. Vaden, Mulugeta Gebregziabher
2018 Frontiers in Psychology  
Together, the results show how multiple imputation can be applied to the classification of cases with missing data and can increase the integrity of results from multi-site open access datasets.  ...  Here we show that reading profiles can be reliably identified based on Random Forest classification of incomplete behavioral datasets, after the missForest method is used to multiply impute missing values  ...  by comparing the synthesized expert labels to the Random Forest classifications.  ... 
doi:10.3389/fpsyg.2018.00644 pmid:29867632 pmcid:PMC5952106 fatcat:cdl24vlh6vgyvhv4m7lq5xjqxi

Distribution-based Label Space Transformation for Multi-label Learning [article]

Zongting Lyu, Yan Yan, Fei Wu
2018 arXiv   pre-print
By defining the distribution based on the similarity of label vectors, a more comprehensive label structure can be captured.  ...  The key to successful multi-label learning algorithms lies in the exploration of inter-label correlations, which usually incur great computational cost.  ...  compare the performance of these multi-label classification methods.  ... 
arXiv:1805.05687v1 fatcat:363yumuqizgfll42q2lbjcgm5m

MORONET: Multi-omics Integration via Graph Convolutional Networks for Biomedical Data Classification [article]

Tongxin Wang, Wei Shao, Zhi Huang, Haixu Tang, Zhengming Ding, Kun Huang
2020 bioRxiv   pre-print
We present a novel multi-omics integrative method named Multi-Omics gRaph cOnvolutional NETworks (MORONET) for biomedical classification.  ...  To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis for multiple types  ...  Acknowledgements This work was supported by Indiana University Precision Health Initiative and National Institute of Biomedical Imaging and Bioengineering (R01EB025018).  ... 
doi:10.1101/2020.07.02.184705 fatcat:lemnficphncsxdkayxyruligbm

Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation

Tong Shen, Guosheng Lin, Chunhua Shen, Ian Reid
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
With comprehensive experiments, we show that the dense multi-label can successfully remove the implausible labels and clear the confusion so as to boost the performance of semantic segmentation systems  ...  Per-pixel semantic labelling of an image benefits greatly from the ability to consider region consistency both locally and globally.  ...  Multi-label classification has also been widely studied.  ... 
doi:10.24963/ijcai.2017/377 dblp:conf/ijcai/ShenLS017 fatcat:qydvclos2vg7vdlkpcznk7imna

Examining Swarm Intelligence-based Feature Selection for Multi-Label Classification

Awder Mohammed Ahmed, Duhok Polytechnic University, Duhok, Kurdistan Region-IRAQ, Adnan Mohsin Abdulazeez, Duhok Polytechnic University, Duhok, Kurdistan Region-IRAQ
2021 Journal of Soft Computing and Data Mining  
Thus, in this paper, we provide a comprehensive review of different swarm intelligence and evolutionary computing methods of feature selection presented for multi-label classification tasks.  ...  We then provided the main characteristics of the existing multi-label feature selection techniques and compared them analytically.  ...  Acknowledgment The authors would like to acknowledge Duhok Polytechnic University/ Technical College of Informatics-Akre.  ... 
doi:10.30880/jscdm.2021.02.02.006 fatcat:biacmvnfmfafhanblgijwki2ta

Improving Machine Reading Comprehension with Multi-Task Learning and Self-Training

Jianquan Ouyang, Mengen Fu
2022 Mathematics  
Therefore, to meet the comprehensive requirements in such application situations, we construct a multi-task fusion training reading comprehension model based on the BERT pre-training model.  ...  In the training phase, since our model requires a large amount of labeled training data, which is often expensive to obtain or unavailable in many tasks, we additionally use self-training to generate pseudo-labeled  ...  results of three multi-task learning methods.  ... 
doi:10.3390/math10030310 fatcat:4gc3cwac5bd3tck57pnicdbfse

Effectiveness of Semi-Supervised Learning and Multi-Source Data in Detailed Urban Landuse Mapping with a Few Labeled Samples

Bo Sun, Yang Zhang, Qiming Zhou, Xinchang Zhang
2022 Remote Sensing  
This study aims to explore the effectiveness of a semi-supervised classification framework with multi-source data for detailed urban landuse classification with a few labeled samples.  ...  Therefore, previous studies on urban landuse classification have often relied on a small size of labeled samples with very uneven spatial distribution.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs14030648 fatcat:xdxqaaz42zbahp4mlbned24oxu

HPSLPred: An Ensemble Multi-label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source [article]

Shixiang Wan, Quan Zou
2017 arXiv   pre-print
First, most of the existing techniques are designed to deal with multi-class rather than multi-label classification, which ignores connections between multiple labels.  ...  For solving these two issues, we have developed an ensemble multi-label classifier called HPSLPred, which can be applied for multi-label classification with an imbalanced protein source.  ...  Acknowledgements The work was supported by the Natural Science Foundation of China (No. 61370010) and the Natural Science Foundation of Fujian Province of China (No.2014J01253).  ... 
arXiv:1704.05204v1 fatcat:j3dqu23mhfd4zpqivgukrghmqy

Empirical Studies on Multi-label Classification

Tao Li, Chengliang Zhang, Shenghuo Zhu
2006 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)  
In this paper, we present a comparative study on various multi-label approaches using both gene and scene data sets.  ...  Our experiments demonstrate that the combined method can take the advantages of the single approaches.  ...  Methods Description We use six different multi-label classification approaches in our comparative study.  ... 
doi:10.1109/ictai.2006.55 dblp:conf/ictai/LiZZ06 fatcat:ps7pggc4m5gy5lle67ptrvv2ey

GCN-IA: User Profile Based on Graph Convolutional Network with Implicit Association Labels [chapter]

Jie Wen, Lingwei Wei, Wei Zhou, Jizhong Han, Tao Guo
2020 Lecture Notes in Computer Science  
On four real-world datasets in Weibo, experimental results demonstrate that GCN-IA produces a significant improvement compared with some state-of-the-art methods.  ...  Current researches on multi-label user profile either ignore the implicit associations among labels or do not consider the user and label semantic information in the social networks.  ...  Classification module makes multi-label classification based on user representations to predict unlabeled user profiles.  ... 
doi:10.1007/978-3-030-50420-5_26 fatcat:jzzj2bqscreurkw5o7n7jpbrkm

Multi-Label Classification of Fake News on Social-Media

Ashwini Deshmukh, Garima Mahto, Harsh Dedhia, Jyoti Sharma
2022 Zenodo  
remarks and articulations, and to empower them to find the specific wellspring of information that isn't affected by others.  ...  Individuals experience passionate feelings for straightforward alluring contents or plans, and thus, they become casualties of it.  ...  Our research team concentrated mostly on the problem transformation method of multi-label classification.  ... 
doi:10.5281/zenodo.6539309 fatcat:7waxw3rryvhcxgrkqxkack6k3e
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