Fine-Grained Algorithm for Improving KNN Computational Performance on Clinical Trials Text Classification

Text classification is an important component in many applications. Text classification has attracted the attention of researchers to continue to develop innovations and build new classification models that are sourced from clinical trial texts. In building classification models, many methods are us...

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Bibliographic Details
Published inBig data and cognitive computing Vol. 5; no. 4; p. 60
Main Authors Jasmir, Jasmir, Nurmaini, Siti, Tutuko, Bambang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2021
Subjects
Accuracy
Algorithms
Artificial intelligence
Classification
Clinical trials
Datasets
Deep learning
fine-grained algorithm
improving computational performance
KNN
Labeling
Supervised learning
Support vector machines
Text categorization
text classification
Texts
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Summary:Text classification is an important component in many applications. Text classification has attracted the attention of researchers to continue to develop innovations and build new classification models that are sourced from clinical trial texts. In building classification models, many methods are used, including supervised learning. The purpose of this study is to improve the computational performance of one of the supervised learning methods, namely KNN, in building a clinical trial document text classification model by combining KNN and the fine-grained algorithm. This research contributed to increasing the computational performance of KNN from 388,274 s to 260,641 s in clinical trial texts on a clinical trial text dataset with a total of 1,000,000 data.
ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc5040060