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14 results for “hierarchical classification”
Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus
<p>We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. We applied our model to classify clinical trial (CT) protocols into completed and terminated categories. We use bag-of-words based as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scores $\simeq 0.85$ on a publicly available large scale CT registry of around 360K protocols. We further demonstrate how the selective pooling can add insights into the CT termination status prediction.</p>
Hierarchical Text Classification corpora
<p>A set of 3 datasets for Hierarchical Text Classification (HTC), with samples divided into training and testing splits. The hierarchies of labels within all datasets have depth 2.</p> <ul> <li>The <strong>Amazon5x5</strong> dataset contains 500,000 user reviews tagged with the reviewed product's categories. There are 5 product categories with 100,000 examples each, and each category has 5 sub-categories.</li> <li>The <strong>Bugs</strong> dataset contains 30,050 bugs of the Linux kernel, labeled with exactly two categories identifying the affected component.</li> <li>Finally, the <strong>Web Of Science</strong> dataset contains 46,960 abstracts of scientific papers, labeled the article's domain (see <a href="https://data.mendeley.com/datasets/9rw3vkcfy4/6">original repo</a> for more details).</li> </ul> <p>Datasets are published in JSONL format, where each line is a string formatted as a JSON, like in the example below.</p> <pre><code>{ "text": <article text>, "labels": [<label1>, <label2>, ...] }</code></pre> <p>The <em>hierarchical structure</em> of labels in each dataset is documented in <a href="https://gitlab.com/distration/dsi-nlp-publib/-/tree/main/htc-survey-24/data/taxonomies">this repository</a>.</p> <p> </p> <p>These datasets have been presented in this paper:</p> <ul> <li>"Hierarchical Text Classification and its Foundations: a Review of Current Research" - DOI: <a href="https://doi.org/10.3390/electronics13071199">10.3390/electronics13071199</a></li> </ul> <p>Some of these datasets have also been used in:</p> <ul> <li>"Ticket Automation: an Insight into Current Research with Applications to Multi-level Classification Scenarios" - DOI: <a href="https://doi.org/10.1016/j.eswa.2023.119984">10.1016/j.eswa.2023.119984</a></li> <li>"A multi-level approach for hierarchical Ticket Classification", accepted at WNUT 2022 - <a href="https://aclanthology.org/2022.wnut-1.22/">link</a></li> </ul> <p> </p> <p>These datasets are partially derived from previous work, namely:</p> <ul> <li>[Amazon] J. Ni, J. Li, J. McAuley, "Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects", EMNLP 2019, doi: <a href="http://dx.doi.org/10.18653/v1/D19-1018">10.18653/v1/D19-1018</a></li> <li>[WOS] K. Kowsari, D. E. Brown, M. Heidarysafa, K. Jafari Meimandi, M. S. Gerber and L. E. Barnes, "HDLTex: Hierarchical Deep Learning for Text Classification," 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017, pp. 364-371, doi: <a href="http://doi.org/10.1109/ICMLA.2017.0-134">10.1109/ICMLA.2017.0-134</a></li> <li>[Linux Bugs] V. Lyubinets, T. Boiko and D. Nicholas, "Automated Labeling of Bugs and Tickets Using Attention-Based Mechanisms in Recurrent Neural Networks," <em>2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP)</em>, 2018, pp. 271-275, doi: <a href="http://doi.org/10.1109/DSMP.2018.8478511">10.1109/DSMP.2018.8478511</a></li> </ul>
The Category-Modifier system: a hierarchical classification scheme for vertebrate tooth marks - supplementary tables
<p>Preserved records of tooth-bone interactions, known as tooth marks, can yield a wealth of information regarding organismal behavior and ecology. For this reason, workers in a wide range of disciplines, but particularly paleontology, have inspected and interpreted these features for decades. Although previous studies have gleaned invaluable insights, they have also described tooth marks using terminological frameworks that have been incompletely defined, have incorporated behavioral hypotheses in definitions, and/or have been inconsistently applied. To address these problems, we introduce the Category-Modifier (CM) system, the first system to both sort tooth marks into clearly defined main categories and use descriptive modifiers to characterize their appearance more precisely. The CM system is designed to apply to a wide range of vertebrates, to enable comparisons across disciplines and studies, and to help researchers keep their investigations into behavioral hypotheses free of circular reasoning.</p>
Copy number variation heterogeneity & hierarchical cancer classifications
<p>This is the CNV data from progenetix on the NCIt morphology tree used in the study Copy number variation heterogeneity reveals biological inconsistency in hierarchical cancer classifications. The columns are separated by tabs, and the values indicate the max CNV value of the biosample on the corresponding bin (1MB) of the genome.</p>
The Category-Modifier system: a hierarchical classification scheme for vertebrate tooth marks - supplementary tables
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Supplementary material 3 from: Grunewald K, Schweppe-Kraft B, Syrbe R-U, Meier S, Krüger T, Schorcht M, Walz U (2020) Hierarchical classification system of Germany's ecosystems as basis for an ecosystem accounting – methods and first results. One Ecosystem 5: e50648. https://doi.org/10.3897/oneeco.5.e50648
The area and share of main ecosystem types and sub ecosystem types (ETs, see Tab. 1)) in the German land cover model (LBM-DE) for the time periods 2012, 2015 and 2018. Linear elements such as small scale structures and infrastructures from the topographic-cartographic Information system (ATKIS) were added to the land cover model.
Supplementary material 4 from: Grunewald K, Schweppe-Kraft B, Syrbe R-U, Meier S, Krüger T, Schorcht M, Walz U (2020) Hierarchical classification system of Germany's ecosystems as basis for an ecosystem accounting – methods and first results. One Ecosystem 5: e50648. https://doi.org/10.3897/oneeco.5.e50648
Tab. D: Detailed matrix of pre- and post-use of settlement and transportation areas in Germany in the period 2013-2018 in hectare per day (ha/d). (Data source: IOER)
Supplementary material 1 from: Grunewald K, Schweppe-Kraft B, Syrbe R-U, Meier S, Krüger T, Schorcht M, Walz U (2020) Hierarchical classification system of Germany's ecosystems as basis for an ecosystem accounting – methods and first results. One Ecosystem 5: e50648. https://doi.org/10.3897/oneeco.5.e50648
Proposal of a classification system for ecosystem types (ETs) in Germany, assignment to the European ecosystem types according to EUNIS and to the CLC types of the database LBM-DE
Supplementary material 2 from: Grunewald K, Schweppe-Kraft B, Syrbe R-U, Meier S, Krüger T, Schorcht M, Walz U (2020) Hierarchical classification system of Germany's ecosystems as basis for an ecosystem accounting – methods and first results. One Ecosystem 5: e50648. https://doi.org/10.3897/oneeco.5.e50648
Supplementation of ecosystem types (ETs) by more differentiated spatially and non-spatially explicit data (system of assignment of biotope and habitat types relevant for nature conservation to ETs
Data from: A hierarchical Bayesian approach for handling missing classification data
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HiPHD: Hierarchical Classification for Protein Remote Homology Detection using Graph Neural Networks and Language Models
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MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification
<p><strong>Overview</strong></p> <p>This dataset contains 10,917 news articles with hierarchical news categories collected between January 1st 2019, and December 31st 2019 classified by using NewsCodes Media Topic taxonomy. We manually labelled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories.</p> <p>This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news.</p> <p><strong>Reproducibility of results</strong></p> <p>The results presented in the research paper "MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification", technical validation can be reproduced using functions in <a href="https://github.com/alinapetukhova/mn-ds-news-classification">github repository</a>.</p> <p><strong>Licenses</strong></p> <p>The dataset is made available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license (see `LICENSE_DATA.txt`).</p>
Multimodal hierarchical classification allows for efficient annotation of CITE-seq data
GEO Series GSE229791. Homo sapiens. 102 samples. Type: Expression profiling by high throughput sequencing; Other.
HiCervix: An Extensive Hierarchical Dataset and Benchmark for Cervical Cytology Classification
<p>In this paper, we release the largest three-level hierarchical cervical dataset (HiCervix), and propose a hierarchical vision transformer-based classification benchmark method (HierSwin).</p> <div> <h3>HiCervix Dataset:</h3> </div> <p>HiCervix includes 40,229 cervical cells and is categorized into 29 annotated classes. These classes are organized within a three-level hierarchical tree to capture fine-grained subtype information.</p> <p> </p> <div> <h3>Citation</h3> </div> <p>Please use below to cite this paper if you find our work useful in your research.</p> <p>Cai D, Chen J, Zhao J, Xue Y, Yang S, Yuan W, Feng M, Weng H, Liu S, Peng Y, Zhu J, Wang K, Jackson C, Tang H, Huang J, Wang X. HiCervix: An Extensive Hierarchical Dataset and Benchmark for Cervical Cytology Classification. IEEE Trans Med Imaging. 2024 Jun 26;PP. doi: 10.1109/TMI.2024.3419697. Epub ahead of print. PMID: 38923481.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.