面向非专家的自适应机器学习工具，简化用户中心模型开发
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI Researchers
- 基于自动化特征工程与增量学习，动态适配不同数据场景
- 支持用户行为个性化建模，减少手动调参与实验耗时
- 专为人机交互研究设计，无需AI背景也能快速上手
机器学习在各领域的广泛应用凸显了非专家可使用的易用系统的重要性。为应对这一需求，自动化机器学习（AutoML）发展出简化机器学习流程的工具。然而，现有AutoML方案在构建在线流水线和人机交互（HCI）应用中的效率与易用性仍不足。为此，本文提出AdaptoML-UX，一个融合自动化特征工程、机器学习与增量学习的自适应框架，帮助非AI专家构建以用户为中心的稳健模型。该工具在多种问题域与数据集上表现出高效适应能力，尤其在人机交互领域显著减少手动实验，节省时间和资源。同时支持通过增量学习实现模型个性化，根据个体用户行为动态优化。研究人员可无需专业背景，借助此工具自动完成算法选择、特征工程与超参数调优，基于数据特性智能生成模型。
原文摘要 · Abstract (English)
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.
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