Integrating Unsupervised and Supervised ML Models for Analysis of Synthetic Data From VAE, GAN, and Clustering of Variables

Integrating Unsupervised and Supervised ML Models for Analysis of Synthetic Data From VAE, GAN, and Clustering of Variables

Lakshmi Prayaga, Krishna Devulapalli, Chandra Prayaga, Aaron Wade, Gopi Shankar Reddy, Sri Satya Harsha Pola
Copyright: © 2024 |Pages: 19
DOI: 10.4018/IJDA.343311
Article PDF Download
Open access articles are freely available for download

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.
Article Preview
Top

Contribution To The Literature

Our contribution to this body of literature encompasses several novel aspects, which are discussed below. First, we introduce a unique integration of unsupervised techniques, such as clustering, with supervised methods, such as classification. This fusion not only represents an innovative approach but also signifies a comprehensive strategy aimed at enhancing classification accuracy by leveraging the strengths of both types of algorithms.

Complete Article List

Search this Journal:
Reset
Volume 5: 1 Issue (2024)
Volume 4: 1 Issue (2023)
Volume 3: 2 Issues (2022): 1 Released, 1 Forthcoming
Volume 2: 2 Issues (2021)
Volume 1: 2 Issues (2020)
View Complete Journal Contents Listing