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TextNetTopics: Text Classification Based Word Grouping as Topics and Topics’ Scoring

Medical document classification is one of the active research problems and the most challenging within the text classification domain. Medical datasets often contain massive feature sets where many features are considered irrelevant, redundant, and add noise, thus, reducing the classification perfor...

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Detalles Bibliográficos
Autores principales: Yousef, Malik, Voskergian, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9251539/
https://www.ncbi.nlm.nih.gov/pubmed/35795215
http://dx.doi.org/10.3389/fgene.2022.893378
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author Yousef, Malik
Voskergian, Daniel
author_facet Yousef, Malik
Voskergian, Daniel
author_sort Yousef, Malik
collection PubMed
description Medical document classification is one of the active research problems and the most challenging within the text classification domain. Medical datasets often contain massive feature sets where many features are considered irrelevant, redundant, and add noise, thus, reducing the classification performance. Therefore, to obtain a better accuracy of a classification model, it is crucial to choose a set of features (terms) that best discriminate between the classes of medical documents. This study proposes TextNetTopics, a novel approach that applies feature selection by considering Bag-of-topics (BOT) rather than the traditional approach, Bag-of-words (BOW). Thus our approach performs topic selections rather than words selection. TextNetTopics is based on the generic approach entitled G-S-M (Grouping, Scoring, and Modeling), developed by Yousef and his colleagues and used mainly in biological data. The proposed approach suggests scoring topics to select the top topics for training the classifier. This study applied TextNetTopics to textual data to respond to the CAMDA challenge. TextNetTopics outperforms various feature selection approaches while highly performing when applying the model to the validation data provided by the CAMDA. Additionally, we have applied our algorithm to different textual datasets.
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spelling pubmed-92515392022-07-05 TextNetTopics: Text Classification Based Word Grouping as Topics and Topics’ Scoring Yousef, Malik Voskergian, Daniel Front Genet Genetics Medical document classification is one of the active research problems and the most challenging within the text classification domain. Medical datasets often contain massive feature sets where many features are considered irrelevant, redundant, and add noise, thus, reducing the classification performance. Therefore, to obtain a better accuracy of a classification model, it is crucial to choose a set of features (terms) that best discriminate between the classes of medical documents. This study proposes TextNetTopics, a novel approach that applies feature selection by considering Bag-of-topics (BOT) rather than the traditional approach, Bag-of-words (BOW). Thus our approach performs topic selections rather than words selection. TextNetTopics is based on the generic approach entitled G-S-M (Grouping, Scoring, and Modeling), developed by Yousef and his colleagues and used mainly in biological data. The proposed approach suggests scoring topics to select the top topics for training the classifier. This study applied TextNetTopics to textual data to respond to the CAMDA challenge. TextNetTopics outperforms various feature selection approaches while highly performing when applying the model to the validation data provided by the CAMDA. Additionally, we have applied our algorithm to different textual datasets. Frontiers Media S.A. 2022-06-20 /pmc/articles/PMC9251539/ /pubmed/35795215 http://dx.doi.org/10.3389/fgene.2022.893378 Text en Copyright © 2022 Yousef and Voskergian. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Yousef, Malik
Voskergian, Daniel
TextNetTopics: Text Classification Based Word Grouping as Topics and Topics’ Scoring
title TextNetTopics: Text Classification Based Word Grouping as Topics and Topics’ Scoring
title_full TextNetTopics: Text Classification Based Word Grouping as Topics and Topics’ Scoring
title_fullStr TextNetTopics: Text Classification Based Word Grouping as Topics and Topics’ Scoring
title_full_unstemmed TextNetTopics: Text Classification Based Word Grouping as Topics and Topics’ Scoring
title_short TextNetTopics: Text Classification Based Word Grouping as Topics and Topics’ Scoring
title_sort textnettopics: text classification based word grouping as topics and topics’ scoring
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9251539/
https://www.ncbi.nlm.nih.gov/pubmed/35795215
http://dx.doi.org/10.3389/fgene.2022.893378
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