ArticleFrontiers in genetics2022
TextNetTopics: Text Classification Based Word Grouping as Topics and Topics' Scoring.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- [Research on the classification method of unbalanced data for the assessment of traffic accident disability level].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Clinical decision support for vestibular diagnosis: large-scale machine learning with lived experience coaching.NPJ digital medicine · 2025Article
- RCE-IFE: recursive cluster elimination with intra-cluster feature elimination.PeerJ. Computer science · 2025Article
- A topic modeling approach for analyzing and categorizing electronic healthcare documents in Afaan Oromo without label information.Scientific reports · 2024Article
- Topic selection for text classification using ensemble topic modeling with grouping, scoring, and modeling approach.Scientific reports · 2024Article
- GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning.Frontiers in genetics · 2023Article
- TextNetTopics Pro, a topic model-based text classification for short text by integration of semantic and document-topic distribution information.Frontiers in genetics · 2023Article
- microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach.Frontiers in microbiology · 2023Article
- Review
- Invention of 3Mint for feature grouping and scoring in multi-omics.Frontiers in genetics · 2023Article
- GediNET for discovering gene associations across diseases using knowledge based machine learning approach.Scientific reports · 2022Article
- miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning.Frontiers in genetics · 2022Article
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2 authors.
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Abstract
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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