ModSSC统一半监督分类框架，支持异构数据与可复现实验。
ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data
- 模块化设计，组件可复用，实验通过配置文件定义。
- 支持多种数据模态，可系统比较不同模型与数据集表现。
- 适合研究人员快速复现基线，提升实验可重复性。
半监督分类利用标注与未标注数据提升预测性能，但现有软件支持在方法、学习设置和数据模态间分散。我们提出 ModSSC，一个开源 Python 框架，用于归纳与直推式半监督分类，旨在支持可复现且受控的实验。ModSSC 提供模块化、可扩展的软件架构，核心为可复用的半监督学习组件、稳定的抽象接口和完全声明式的实验配置。实验通过配置文件定义，无需修改算法代码即可在异构数据集与模型主干间进行系统性对比。ModSSC 1.0.0 已以 MIT 许可证发布，附完整文档与自动化测试，项目地址：https://github.com/ModSSC/ModSSC。框架通过受控实验验证，成功复现了多种数据模态下的经典半监督学习基线。
原文摘要 · Abstract (English)
Semi-supervised classification leverages both labeled and unlabeled data to improve predictive performance, but existing software support remains fragmented across methods, learning settings, and data modalities. We introduce ModSSC, an open source Python framework for inductive and transductive semi-supervised classification designed to support reproducible and controlled experimentation. ModSSC provides a modular and extensible software architecture centered on reusable semi-supervised learning components, stable abstractions, and fully declarative experiment specification. Experiments are defined through configuration files, enabling systematic comparison across heterogeneous datasets and model backbones without modifying algorithmic code. ModSSC 1.0.0 is released under the MIT license with full documentation and automated tests, and is available at https://github.com/ModSSC/ModSSC. The framework is validated through controlled experiments reproducing established semi-supervised learning baselines across multiple data modalities.
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