ArticleJournal of applied statistics2023
Unsupervised document classification integrating web scraping, one-class SVM and LDA topic modelling.
Article in Journal of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- A topic modeling approach for analyzing and categorizing electronic healthcare documents in Afaan Oromo without label information.Scientific reports · 2024Article
- How perceived sustainability influences consumers' clothing preferences.Scientific reports · 2024Article
- Editorial to the special issue: Statistical Approaches for Big Data and Machine Learning.Journal of applied statistics · 2023Article
- An iterative topic model filtering framework for short and noisy user-generated data: analyzing conspiracy theories on twitter.International journal of data science and analytics · 2022Article
- Clinical Text Data Categorization and Feature Extraction Using Medical-Fissure Algorithm and Neg-Seq Algorithm.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unsupervised document classification for imbalanced data sets poses a major challenge. To obtain accurate classification results, training data sets are often created manually by humans which requires expert knowledge, time and money. Depending on the imbalance of the data set, this approach also either requires human labelling of all of the data or it fails to adequately recognize underrepresented categories. We propose an integration of web scraping, one-class Support Vector Machines (SVM) and Latent Dirichlet Allocation (LDA) topic modelling as a multi-step classification rule that circumvents manual labelling. Unsupervised one-class document classification with the integration of out-of-domain training data is achieved and >80% of the target data is correctly classified. The proposed method thus even outperforms common machine learning classifiers and is validated on multiple data sets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.