![[2512.13228] ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2512.13228] ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data
Summary
ModSSC is an open-source Python framework designed for semi-supervised classification, enhancing reproducibility and experimentation across heterogeneous data.
Why It Matters
The introduction of ModSSC addresses the fragmentation in existing semi-supervised learning tools, providing a unified platform that supports both inductive and transductive learning. This framework is significant for researchers and practitioners aiming for reproducible results in machine learning experiments, particularly in diverse data environments.
Key Takeaways
- ModSSC offers a modular architecture for semi-supervised classification.
- It supports both labeled and unlabeled data to improve predictive performance.
- The framework allows for systematic comparison across different datasets without altering code.
- ModSSC is open-source and released under the MIT license, promoting community collaboration.
- Validated through experiments, it reproduces established semi-supervised learning baselines.
Computer Science > Machine Learning arXiv:2512.13228 (cs) [Submitted on 15 Dec 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data Authors:Melvin Barbaux (IMB), Samia Boukir (IMB) View a PDF of the paper titled ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data, by Melvin Barbaux (IMB) and 1 other authors View PDF Abstract: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 this https URL. The framework is validated through controlled experiments reproducing established semi-supervised learning baselines across multiple data modalities. Comments: Su...