KAXAI: An Integrated Environment for Knowledge Analysis and Explainable AI
This work addresses the problem of machine learning accessibility for non-experts, though it appears incremental with hybrid components.
The paper tackled the challenge of making machine learning more accessible by integrating AutoML, XAI, and synthetic data generation into a user-friendly system, achieving 96% accuracy on a diabetes dataset and 93% on a survey dataset with novel classifiers. It also introduced a local interpreter and found that enhancing datasets with GANs is the most reliable method for synthetic data generation.
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.