Semi-supervised learning models for documentclassification: A systematic review and meta-analysis - Cevallos-Culqui, Alex (2023)
The proliferation of digital documents in the internet has given rise to the search for informationpatterns that allow for the categorization of organizational documents to generate knowledge in a institution.One of the Artificial Intelligence techniques for this purpose is text classification, which for its application useslabels (categorized documents) with supervised (with labels) or unsupervised (without labels) training models.Both traditional models with their advantages and disadvantages have been consolidated into semi-supervisedmodels to extract the best qualities of each one, however, the labeling process involves resources that need to beoptimized to improve the classification accuracy. An analysis of the types of semi-supervised models would showthe strengths of their training and how the structure of each of them affects the accuracy of their classification. Thepresent study proposes a structure of semi-supervised model in document classification, in order to analyze thequalities of each one in their categorization process, it through a systematic literature review (SLR) that analyzesthe performance of the studies to conduct a meta-analysis. Further, the study search strategy was defined by thePICOC method (Population, Intervention, Comparison, Outcome, Context), supported by two research questionsand delimited in a search chain that allowed the collection of 332 research studies. These papers were filteredusing the PRISMA method and the determination of exclusion criteria, in total 46 papers have been selected forthe present study.From this SLR, an organizational structure has been obtained for semi-supervised models anda scheme for the classification process. In addition, the advantages and disadvantages of different learning typeshave been analyzed, evaluating their classification performance in each type of learning through a meta-analysis.This has determined that the models that present the best levels of performance are active learning model (0.88)and ensemble learning model (0.84)