Open AccessImproving the quality of predictive models in small data GSDOT: A new algorithm for generating synthetic data
Author(s) -
Georgios Douzas,
Maria Lechleitner,
Fernando Bação
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0265626
Subject(s) - oversampling , computer science , machine learning , algorithm , artificial intelligence , quality (philosophy) , process (computing) , data mining , data quality , small data , metric (unit) , computer network , philosophy , operations management , bandwidth (computing) , epistemology , economics , operating system
In the age of the data deluge there are still many domains and applications restricted to the use of small datasets. The ability to harness these small datasets to solve problems through the use of supervised learning methods can have a significant impact in many important areas. The insufficient size of training data usually results in unsatisfactory performance of machine learning algorithms. The current research work aims to contribute to mitigate the small data problem through the creation of artificial instances, which are added to the training process. The proposed algorithm, Geometric Small Data Oversampling Technique, uses geometric regions around existing samples to generate new high quality instances. Experimental results show a significant improvement in accuracy when compared with the use of the initial small dataset as well as other popular artificial data generation techniques.