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HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification [article]

Pengfei Gao, Jingpeng Zhao, Yinglong Ma, Ahmad Tanvir, Beihong Jin
2022 arXiv   pre-print
Hierarchical multi-label text classification (HMTC) with higher accuracy over large sets of closely related categories organized in a hierarchy or taxonomy has become a challenging problem.  ...  At last, the comprehensive analysis is made based on extensive experiments in comparison with the state-of-the-art hierarchical and flat multi-label text classification approaches over two benchmark datasets  ...  HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification 27 They have no conflicts of interest to declare that are relevant to the content of this article.  ... 
arXiv:2204.08115v1 fatcat:drutc57uvbdfnm3bpun6o53tx4

An Empirical Comparison of Flat and Hierarchical Performance Measures for Multi-Label Classification with Hierarchy Extraction [chapter]

Florian Brucker, Fernando Benites, Elena Sapozhnikova
2011 Lecture Notes in Computer Science  
In contrast to Hierarchical Multi-label Classification (HMC), where the class hierarchy is assumed to be known a priori, we are interested in the opposite case where it is unknown and should be extracted  ...  In this case the predictive performance of a classifier can be assessed by well-known Performance Measures (PMs) used in flat MC such as precision and recall.  ...  In this setting, the predictive performance of a multi-label classifier can be assessed either by well-known Performance Measures (PMs) used in flat multi-label classification such as precision and recall  ... 
doi:10.1007/978-3-642-23851-2_59 fatcat:qfcw3ev6ezazrkrjjkfoowyn3u

Academic Resource Text Level Multi-label Classification based on Attention [article]

Yue Wang, Yawen Li, Ang Li
2022 arXiv   pre-print
Hierarchical multi-label academic text classification (HMTC) is to assign academic texts into a hierarchically structured labeling system.  ...  We propose an attention-based hierarchical multi-label classification algorithm of academic texts (AHMCA) by integrating features such as text, keywords, and hierarchical structure, the academic documents  ...  HMCN [1] integrates the prediction results of each level in the hierarchy and the overall hierarchy for hierarchical multi-label classification.  ... 
arXiv:2203.10743v1 fatcat:badnoix3jveqhn7xc4vqurt5tq

Exploiting Global and Local Hierarchies for Hierarchical Text Classification [article]

Ting Jiang, Deqing Wang, Leilei Sun, Zhongzhi Chen, Fuzhen Zhuang, Qinghong Yang
2022 arXiv   pre-print
Hierarchical text classification aims to leverage label hierarchy in multi-label text classification.  ...  Existing methods encode label hierarchy in a global view, where label hierarchy is treated as the static hierarchical structure containing all labels.  ...  Related Work Hierarchical text classification (HTC) is a special multi-label text classification problem that requires constructing one or more paths from the taxonomic hierarchy in a top-down manner  ... 
arXiv:2205.02613v1 fatcat:7sbc7wexbfc6raa57xvwcdrj6i

Inducing a hierarchy for multi-class classification problems [article]

Hayden S. Helm, Weiwei Yang, Sujeeth Bharadwaj, Kate Lytvynets, Oriana Riva, Christopher White, Ali Geisa, Carey E. Priebe
2021 arXiv   pre-print
In this paper, we investigate a class of methods that induce a hierarchy that can similarly improve classification performance over flat classifiers.  ...  The class of methods follows the structure of first clustering the conditional distributions and subsequently using a hierarchical classifier with the induced hierarchy.  ...  In particular, when a latent hierarchy exists this structure can be inferred and used for hierarchical classification to improve performance in particular parameter settings.  ... 
arXiv:2102.10263v1 fatcat:swvs7o4z5veododdp67q6jks5e

Hierarchical Single Label Classification: An Alternative Approach [chapter]

Esra'a Alshdaifat, Frans Coenen, Keith Dures
2013 Research and Development in Intelligent Systems XXX  
Experimental results show that the proposed mechanism can improve classification performance in terms of average accuracy and average AUC in the context of some data sets.  ...  In this paper an approach to multi-class (as opposed to multi-label) classification is proposed.  ...  improve classification performance [11] , and (ii) dealing with smaller subsets of class labels at each node might produce better results.  ... 
doi:10.1007/978-3-319-02621-3_3 dblp:conf/sgai/AlshdaifatCD13 fatcat:l2wm2ceyendnzoyya4ityzgx7e

On exploiting hierarchical label structure with pairwise classifiers

Johannes Fürnkranz, Jan Frederik Sima
2011 SIGKDD Explorations  
The goal of this work was to test whether the performance of a regular pairwise classifier can be improved when additional information about the hierarchical class structure is added to the training sets  ...  We explain this with the fact that the structure of the class hierarchy is not reflected in the distribution of the instances.  ...  Acknowledgments: This research was supported by the German Science Foundation (DFG). We would like to thank Eyke Hüllermeier for inspiring discussions on this subject.  ... 
doi:10.1145/1964897.1964903 fatcat:vxw53xx6drgwdkdagdev5cdjoe

Capsule Network Algorithm for Performance Optimization of Text Classification

Samuel Manoharan J
2021 Journal of Soft Computing Paradigm  
Classification of hierarchical multi-label text is performed with a simple capsule network algorithm in this paper.  ...  The encoded latent data is combined with the algorithm while handling structurally diverse categories and rare events in hierarchical multi-label text applications.  ...  The HMC task is treated as a multi-label classification problem considering all the labels of the hierarchy.  ... 
doi:10.36548/jscp.2021.1.001 fatcat:thebssmm3bfvrlyctvy4jheqea

All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP) [article]

Ashwin Vaswani, Gaurav Aggarwal, Praneeth Netrapalli, Narayan G Hegde
2022 arXiv   pre-print
This paper considers the problem of Hierarchical Multi-Label Classification (HMC), where (i) several labels can be present for each example, and (ii) labels are related via a domain-specific hierarchy  ...  While there have been works that apply such an idea to single-label classification, to the best of our knowledge, there are limited such works for multilabel classification focusing on the severity of  ...  However, existing work is majorly focused on just using hierarchy to improve learning but has still not focused on the notion of better mistakes in the multi-label domain, motivating us to work on CHAMP  ... 
arXiv:2206.08653v1 fatcat:o5imcpmhjbajbnatnnuejhki3y

Hierarchical Text Classification of Urdu News using Deep Neural Network [article]

Taimoor Ahmed Javed, Waseem Shahzad, Umair Arshad
2021 arXiv   pre-print
The objectives of this paper are twofold: (1) to develop a large human-annotated dataset of news in Urdu language for hierarchical text classification; and (2) to classify Urdu news hierarchically using  ...  To classify large size of corpus, one common approach is to use hierarchical text classification, which aims to classify textual data in a hierarchical structure.  ...  Vector Machines (SVMs): In this method, we perform standard multi-label classification using one-vs-the-rest (OvR) strategy because we have multiple labels, so we use it.  ... 
arXiv:2107.03141v1 fatcat:kxrtu6i7ongzrfcotihjvqvtdu

Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction

Wei Li, Ruihan Bao, Keiko Harimoto, Deli Chen, Jingjing Xu, Qi Su
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
To make use of the connection among stocks, we propose a LSTM Relational Graph Convolutional Network (LSTM-RGCN) model, which models the connection among stocks with their correlation matrix.  ...  One big obstacle for such task is the lacking of data, in this work we collect and publish the overnight stock price movement dataset of Reuters Financial News.  ...  As leveraging label dependencies can greatly improve the performance of multi-label hierarchical classification [Sun and Lim, 2001; Cai and Hofmann, 2004] , proposes ML-Forest to learn an ensemble of  ... 
doi:10.24963/ijcai.2020/619 dblp:conf/ijcai/LiangCYLQZ20 fatcat:fh6zsni4ijgpjdbww4z5j2m7nu

Label Hierarchy Transition: Modeling Class Hierarchies to Enhance Deep Classifiers [article]

Renzhen Wang, De cai, Kaiwen Xiao, Xixi Jia, Xiao Han, Deyu Meng
2021 arXiv   pre-print
of encoding the correlation embedded in class hierarchies.  ...  In this paper, we propose Label Hierarchy Transition, a unified probabilistic framework based on deep learning, to address hierarchical classification.  ...  In [32], the class hierarchies with classification framework for hierarchical classification. a tree structure were used to impose prior on the param- eters of the classification layer for  ... 
arXiv:2112.02353v1 fatcat:ou25wtcmlfepvcnc6hv4eem7ze

New Ideas for Applying Ant Colony Optimization to the Protein Function

Mohana Prabha G., S. Chitra
2018 Journal of Data Processing  
The proposed ACO algorithm discovers an ordered list of hierarchical multi-label classification rules.  ...  an instance of a hierarchical multi-label problem.  ...  Because of that, the entropy measure used in hAnt-Miner is replaced by the distance measure in hmAnt-Miner, which is a more suitable measure for hierarchical multi-label classification.  ... 
doi:10.6025/jdp/2018/8/2/74-81 fatcat:7gkac6vekrai3h47oq5pjbdfou

Hierarchical Class-Based Curriculum Loss [article]

Palash Goyal, Shalini Ghosh
2020 arXiv   pre-print
Classification algorithms in machine learning often assume a flat label space. However, most real world data have dependencies between the labels, which can often be captured by using a hierarchy.  ...  In this paper, we propose a loss function, hierarchical curriculum loss, with two properties: (i) satisfy hierarchical constraints present in the label space, and (ii) provide non-uniform weights to labels  ...  Hierarchical multi-label classification (HMC) methods, which utilize the hierarchy of class labels, aim to tackle the above issue.  ... 
arXiv:2006.03629v1 fatcat:2ih7n7fhxjf7xat5jnkbmd6p64

Generative Multi-task Learning for Text Classification

Wei Zhao, Hui Gao, Shuhui Chen, Nan Wang
2020 IEEE Access  
In the two decoders, a label-orderindependent multi-label classification loss function and a hierarchical structure mask matrix are introduced.  ...  In this paper, a generative multi-task learning (MTL) approach for text classification and categorization is proposed, which is composed of a shared encoder, a multilabel classification decoder and a hierarchical  ...  of the predicted labels in the multi-label classification task, which shows a slightly improvement of the classification performance (Table 2 ) .  ... 
doi:10.1109/access.2020.2991337 fatcat:bdfsl7hrhjgntomapzezuy4hye
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