KAXAI: An Integrated Environment for Knowledge Analysis and Explainable AI

TL;DR

This paper presents KAXAI, an integrated system combining AutoML, XAI, and synthetic data generation to make machine learning more accessible, with novel classifiers and interpretability tools demonstrating high accuracy and effective synthetic data methods.

Contribution

The paper introduces a comprehensive environment that simplifies machine learning for users, featuring new classifiers, interpretability methods, and synthetic data techniques, including GAN-based augmentation.

Findings

GAN-based synthetic data generation is most reliable for quantitative datasets.

The system achieves 96% accuracy on a diabetes dataset and 93% on a survey dataset.

MEDLEY provides effective local interpretation compared to LIME, Greedy, and Parzen.

Abstract

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.