ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data
This provides a modular tool for researchers and practitioners to systematically compare semi-supervised methods, though it is incremental as it focuses on software integration rather than new algorithms.
The authors tackled the problem of fragmented software support for semi-supervised classification by introducing ModSSC, an open-source Python framework that enables reproducible and controlled experimentation across heterogeneous datasets and model backbones.
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.