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Generative Multi-task Learning for Text Classification
2020
IEEE Access
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In the two decoders, a label-orderindependent multi-label classification loss function and a hierarchical structure mask matrix are introduced. ...
INDEX TERMS Generative model, hierarchical classification, multi-label classification, multi-task learning. This work is licensed under a Creative Commons Attribution 4.0 License. ...
The existing research on multi-label text classification and hierarchical classification mainly focus on modeling one-to-many mapping between samples and labels or categories. ...
doi:10.1109/access.2020.2991337
fatcat:bdfsl7hrhjgntomapzezuy4hye
Hierarchical Text Categorization Using Level Based Neural Networks of Word Embedding Sequences with Sharing Layer Information
2018
Walailak Journal of Science and Technology
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Also, a training strategy to avoid imbalance issues is proposed called "the balanced resampling with mini-batch training" Furthermore, a label correction strategy is proposed to conform the predicted results ...
One of the most widely-used approaches is a level-based strategy that induces a multiclass classifier for each class level independently. ...
[3] introduced a Hierarchical Multi-label Classification using Local Multi-Layer Perceptron (HMC-LMLP) to predict the protein function that is a hierarchical classification problem in biology. ...
doi:10.48048/wjst.2019.4145
fatcat:de6vgapbqbgfrhzrbf7hbluifa
A Multi-path Strategy for Hierarchical Ensemble Classification
[chapter]
2014
Lecture Notes in Computer Science
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A solution to the multi-class classification problem is proposed founded on the concept of an ensemble of classifiers arranged in a hierarchical binary tree formation. ...
To address this issue a multi-path strategy is investigated based on the idea of using Classification Association Rule Miners at individual nodes. ...
In the context of the work described in this paper the focus is on the multi-class single-label classification problem where each example is associated with exactly one element of the class label set C ...
doi:10.1007/978-3-319-08979-9_16
fatcat:4trpxyp7x5hdbafmwubuupdi2y
Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework
[article]
2022
arXiv
pre-print
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In this paper, we present a hierarchical multi-label representation learning framework that can leverage all available labels and preserve the hierarchical relationship between classes. ...
The loss function is data driven and automatically adapts to arbitrary multi-label structures. ...
Introduction In the real world, hierarchical multi-labels occur naturally and frequently. Biological classification of organisms is structured in a taxonomic hierarchy. ...
arXiv:2204.13207v1
fatcat:zux6lp24drbbhbbzoflhomhwyi
Improving Seasonal Land Cover Maps of Poyang Lake Area in China by Taking into Account Logical Transitions
2016
ISPRS International Journal of Geo-Information
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The classification performance and the codes for all the seasons are imposed on the initial land cover maps which have been produced independently by the conventional hierarchical strategy. ...
The illogical transitions between neighboring seasons and the accuracies based on the labeled samples are calculated for both the initial and modified strategies. ...
In a word, the classification process is carried out on each image, while the resultant class labels are synthetically decided by the classification results of multi-source data. ...
doi:10.3390/ijgi5090165
fatcat:2bkdiw23vvauxiihc4outfuj7m
Exploiting Label Dependency for Hierarchical Multi-label Classification
[chapter]
2012
Lecture Notes in Computer Science
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Our experimental results on several real-world biomolecular datasets show that the proposed method can improve the performance of hierarchical multi-label classification. ...
Existing hierarchical multi-label classification algorithms ignore possible correlations between the labels. ...
Hierarchical multi-label classification is a variant of traditional classification where the task is to assign instances to a set of labels where the labels are related through a hierarchical classification ...
doi:10.1007/978-3-642-30217-6_25
fatcat:dteak3harzcdngqnkrlc5j4d7e
A Multi-Primitive-Based Hierarchical Optimal Approach for Semantic Labeling of ALS Point Clouds
2019
Remote Sensing
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Then, based on initial labeling results, a novel, hierarchical, and optimal strategy is developed to optimize semantic labeling results. ...
The scores for correctness attained over 98% in all cases of the Vaihingen datasets and up to 96% in the Hong Kong dataset. ...
We also implemented a multi-primitive-based method without the proposed hierarchical optimal strategy and a multi-scaled point-based classification method on the Hong Kong dataset. ...
doi:10.3390/rs11101243
fatcat:st3s6spepvexfejx7qkhwrapr4
Priberam Labs at the NTCIR-15 SHINRA2020-ML: Classification Task
[article]
2021
arXiv
pre-print
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We also test several pooling strategies to leverage BERT's embeddings and selection criteria based on the labels' scores. ...
In this work, we propose three distinct models based on the contextualised embeddings yielded by Multilingual BERT. ...
Note that since the present ontology allows for multi-label classification, an entity can be assigned more than one type. ...
arXiv:2105.05605v1
fatcat:awus4zmdnncq7nezus2j4jwkxe
LSHTC: A Benchmark for Large-Scale Text Classification
[article]
2015
arXiv
pre-print
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All of these datasets are available online and runs may still be submitted on the online server of the challenges. ...
LSHTC is a series of challenges which aims to assess the performance of classification systems in large-scale classification in a a large number of classes (up to hundreds of thousands). ...
Flat evaluation measures are split in to two main families, single-label and multi-label. In single-label classification there is only one gold label for each instance. ...
arXiv:1503.08581v1
fatcat:hgn6xf2x3vclpofkx3yuydwvai
A new approach for multi-label classification based on default hierarchies and organizational learning
2008
Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation - GECCO '08
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This paper considers LCSs as an approach to classification problems, more specifically a more complex kind of classification called multi-label classification. ...
Classification problems are problems where instances of a data set belong to a set of classes, and the system needs to infer, based on past experience, the correct class (or classes) of new, previously ...
MULTI-LABEL CLASSIFICATION Unlike single-label classification, where one instance belongs to just one of the possible classes of the problem, in multi-label classification the classes are not disjoint. ...
doi:10.1145/1388969.1389015
dblp:conf/gecco/VallimGLDC08
fatcat:c6jtfxxd5ra6tiw64fenn5wo5m
Multi-label Classification for Tree and Directed Acyclic Graphs Hierarchies
[chapter]
2014
Lecture Notes in Computer Science
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Hierarchical Multi-label Classification (HMC) is the task of assigning a set of classes to a single instance with the peculiarity that the classes are ordered in a predefined structure. ...
The proposed approach was experimentally evaluated with 10 tree and 8 DAG hierarchical datasets in the domain of protein function prediction. ...
Related Work When the labels in a multi-label classification problems are ordered in a predefined structure, typically a tree or a Direct Acyclic Graph (DAG), the task is called Hierarchical Multi-label ...
doi:10.1007/978-3-319-11433-0_27
fatcat:xlixcx5655eq3lmelqjzcyxgdm
Hierarchical Novelty Detection
[chapter]
2017
Lecture Notes in Computer Science
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Many practical hierarchical classification problems also share features with multi-label classification (i.e., each data point can have any number of labels, even non-hierarchically related) and novelty ...
Hierarchical classification is commonly defined as multi-class classification where the classes are hierarchically nested. ...
the hierarchy for a multi-label classification. ...
doi:10.1007/978-3-319-68765-0_26
fatcat:uxifsqj2z5fpjnapotormsgwue
Hierarchical bilinear convolutional neural network for image classification
2021
IET Computer Vision
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Image classification is one of the mainstream tasks of computer vision. However, the most existing methods use labels of the same granularity level for training. ...
To this end, a multi-task learning framework, named as Hierarchical Bilinear Convolutional Neural Network (HB-CNN), is developed by seamlessly integrating CNNs with multitask learning over the hierarchical ...
It can be seen from Table 3 that the hierarchical training strategy (MBDJ-strategy) can effectively boost the classification accuracy. ...
doi:10.1049/cvi2.12023
fatcat:apr3ebtwdreohivpdol6um72ai
Hierarchical Multi-Label Object Detection Framework for Remote Sensing Images
2020
Remote Sensing
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, and (2) a hierarchical sibling classification network for supporting hierarchical multi-label classification. ...
We propose a hierarchical multi-label object detection framework applicable to hierarchically partial-annotated datasets. ...
supporting hierarchical multi-label classification. ...
doi:10.3390/rs12172734
fatcat:yjspwtjz4bhtpcfahnpiarz4wa
Constrained Sequence-to-Tree Generation for Hierarchical Text Classification
[article]
2022
arXiv
pre-print
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The majority of prior studies consider HTC as a flat multi-label classification problem, which inevitably leads to "label inconsistency" problem. ...
Hierarchical Text Classification (HTC) is a challenging task where a document can be assigned to multiple hierarchically structured categories within a taxonomy. ...
INTRODUCTION Hierarchical text classification (HTC) is a particular multi-label text classification problem, which aims to assign each document to a set of relevant nodes of a taxonomic hierarchy as depicted ...
arXiv:2204.00811v2
fatcat:qdeai4rkxnezhj5opbligqqiwi
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