KAXAI: An Integrated Environment for Knowledge Analysis and Explainable
  AI
Saikat Barua Dr. Sifat Momen
In order to fully harness the potential of machine learning, it is crucial to
establish a system that renders the field more accessible and less daunting for
individuals who may not possess a comprehensive understanding of its
intricacies. The paper describes the design of a system that integrates AutoML,
XAI, and synthetic data generation to provide a great UX design for users. The
system allows users to navigate and harness the power of machine learning while
abstracting its complexities and providing high usability. The paper proposes
two novel classifiers, Logistic Regression Forest and Support Vector Tree, for
enhanced model performance, achieving 96\% accuracy on a diabetes dataset and
93\% on a survey dataset. The paper also introduces a model-dependent local
interpreter called MEDLEY and evaluates its interpretation against LIME,
Greedy, and Parzen. Additionally, the paper introduces LLM-based synthetic data
generation, library-based data generation, and enhancing the original dataset
with GAN. The findings on synthetic data suggest that enhancing the original
dataset with GAN is the most reliable way to generate synthetic data, as
evidenced by KS tests, standard deviation, and feature importance. The authors
also found that GAN works best for quantitative datasets.