Summary of Kaxai: An Integrated Environment For Knowledge Analysis and Explainable Ai, by Saikat Barua et al.
KAXAI: An Integrated Environment for Knowledge Analysis and Explainable AI
by Saikat Barua, Sifat Momen
First submitted to arxiv on: 30 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning educators writing for a technical audience can expect this paper to provide a system that combines AutoML, XAI, and synthetic data generation to create an intuitive user experience. The proposed system abstracts away machine learning complexities while offering high usability. Two novel classifiers, Logistic Regression Forest and Support Vector Tree, are introduced, achieving 96% accuracy on a diabetes dataset and 93% on a survey dataset. Additionally, the paper presents MEDLEY, a local interpreter for evaluating model performance against LIME, Greedy, and Parzen. The authors also explore synthetic data generation using LLM-based methods, library-based approaches, and GAN-enhanced datasets, finding that GAN performs best for quantitative datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is like trying to solve a big puzzle! This paper helps make machine learning easier to understand by creating a system that makes it more accessible. It’s like having a guide who explains things in simple terms so you can focus on solving the puzzle, not getting lost in complicated details. The authors came up with new ways to improve model performance and even created new tools to help us understand how they work. They also experimented with generating fake data that looks real, finding that one way is better than others for certain types of puzzles. |
Keywords
* Artificial intelligence * Gan * Logistic regression * Machine learning * Synthetic data
