AdaptoML-UX：面向非AI专家与HCI研究者的自适应、以用户为中心的GUI自动化机器学习工具包
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI Researchers
AI总结：
本文提出AdaptoML-UX自适应框架，集成自动化特征工程、机器学习与增量学习，帮助非AI专家和HCI研究者高效构建以用户为中心且可个性化的ML模型，降低手动实验成本。
摘要：
机器学习在各个领域的日益融合凸显了对非专家也能有效使用的可访问系统的需求。为满足这一需求，自动化机器学习（AutoML）领域已开发出简化ML流水线构建与优化的工具。然而，现有AutoML解决方案在创建在线流水线的效率以及面向人机交互（HCI）应用的易用性方面往往存在不足。因此，本文提出AdaptoML-UX，一个自适应框架，融合了自动化特征工程、机器学习和增量学习，以帮助非AI专家开发稳健的、以用户为中心的ML模型。该工具包展示了高效适应不同问题领域和数据集的能力，尤其是在HCI领域，从而减少手动实验的必要性并节省时间与资源。此外，它通过增量学习支持模型个性化，使模型适应个体用户行为。HCI研究者无需专门专业知识即可使用AdaptoML-UX（\url{https://github.com/MichaelSargious/AdaptoML_UX}），因为它能根据数据的独特特征自动完成算法选择、特征工程和超参数调优。
英文摘要：
The increasing integration of machine learning across various domains has underscored the necessity for accessible systems that non-experts can utilize effectively. To address this need, the field of automated machine learning (AutoML) has developed tools to simplify the construction and optimization of ML pipelines. However, existing AutoML solutions often lack efficiency in creating online pipelines and ease of use for Human-Computer Interaction (HCI) applications. Therefore, in this paper, we introduce AdaptoML-UX, an adaptive framework that incorporates automated feature engineering, machine learning, and incremental learning to assist non-AI experts in developing robust, user-centered ML models. Our toolkit demonstrates the capability to adapt efficiently to diverse problem domains and datasets, particularly in HCI, thereby reducing the necessity for manual experimentation and conserving time and resources. Furthermore, it supports model personalization through incremental learning, customizing models to individual user behaviors. HCI researchers can employ AdaptoML-UX (\url{https://github.com/MichaelSargious/AdaptoML_UX}) without requiring specialized expertise, as it automates the selection of algorithms, feature engineering, and hyperparameter tuning based on the unique characteristics of the data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
