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Integrating Unsupervised and Supervised ML Models for Analysis of Synthetic Data From VAE, GAN, and Clustering of Variables
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Author(s): Lakshmi Prayaga (University of West Florida, USA), Krishna Devulapalli (Indian Institute of Chemical Technology, India), Chandra Prayaga (University of West Florida, USA), Aaron Wade (University of West Florida, USA), Gopi Shankar Reddy (University of West Florida, USA)and Sri Satya Harsha Pola (University of West Florida, USA)
Copyright: 2024
Volume: 5
Issue: 1
Pages: 19
Source title:
International Journal of Data Analytics (IJDA)
Editor(s)-in-Chief: Bruce Qiang Swan (SUNY Buffalo State, USA)
DOI: 10.4018/IJDA.343311
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Abstract
Clustering of variables is a specialized approach for dimensionality reduction. This strategy is evaluated for data reduction with a Kaggle diabetes dataset. Since the original dataset is small, Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) are used to generate 100,000 records and tested for resemblance to the real data using standard statistical methods. VAE-data is more representative of the real data than GAN-data when analyzed using machine learning (ML) models. Applying Clustering of Variables on VAE-data yields new synthetic variables (SV). SV-data is then augmented with target variable data. Random Forest model is used on VAE and SV data. SV-data results matched VAE-data, proving the new data's quality. SV-data also provides insights into correlations and data dispersion patterns. This analysis implements a combination of Unsupervised learning (clustering of variables) and Supervised learning (classification) which is reflected in the results.
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