为机器学习新手提供可透明操作的智能解决方案推荐平台
Public Machine Learning Solver Framework for Novices in the Machine Learning Domain
- 融合专家经验与自动数据分析，生成定制化机器学习流程
- 支持多准则评估并按相关性排序算法，提升推荐准确性
- 适合初学者和需要快速验证方案的研究者使用
解决机器学习问题复杂且通常仅限专家。过去二十年出现多种支持非专家的系统，我们将其归纳为三类：(1) 全自动AutoML系统，(2) 算法选择专家速查表，(3) 基于准确率、可解释性、数据需求等标准的决策支持系统。本文提出一个新平台，整合第2类与第3类，为非专家提供半自动化、智能化的解决方案推荐。不同于仅推荐单一算法的现有方法，本平台建议完整流水线，结合专家定义的选择标准与迁移学习，自动从用户数据中提取特征（如类别不平衡、缺失值）。平台采用一阶逻辑推理知识库，按相关性排序推荐算法。具备友好界面，并连接众包平台以持续更新。系统分步构建，支持无缝集成新算法、标准与领域知识。据我们所知，这是首个免费公开在线框架，系统化捕获并实现专家知识，以结构化、透明方式指导非专家解决机器学习问题。
原文摘要 · Abstract (English)
Solving machine learning problems is complex and typically reserved for experts. Over the past two decades, systems have emerged to support non-experts. Based on our review, we identify three categories: (1) fully automated AutoML systems, (2) expert cheat sheets for algorithm selection, and (3) decision-support systems using selection criteria (accuracy, transparency, data requirements). We propose a new platform combining categories 2 and 3 to deliver semi-automated, intelligent solution recommendations for non-experts. Unlike existing approaches that recommend a single algorithm, our platform suggests a complete pipeline tailored to the user's problem. It integrates expert-defined selection criteria with transfer learning and automatically extracts data characteristics (e.g., class imbalance, missing values) from user-provided datasets. The platform uses first-order logic to reason over its knowledge base and recommends suitable algorithms ranked by relevance. It features a user-friendly interface and connects to a crowdsourcing platform for ML experts, ensuring continuous updates. The platform is built incrementally, allowing seamless integration of new algorithms, criteria, and domain knowledge. To our knowledge, this is the first free, publicly accessible online framework that systematically captures and operationalizes expert knowledge to guide non-experts in solving ML problems in a structured, transparent manner.
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