ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data

TL;DR

ModSSC is an open source, modular Python framework designed to facilitate reproducible semi-supervised classification experiments across diverse data types and models, promoting systematic comparison and extensibility.

Contribution

It introduces a flexible, declarative software architecture for semi-supervised learning, supporting reproducibility and experimentation without altering core algorithms.

Findings

Validated through experiments reproducing established baselines

Supports heterogeneous datasets and model architectures

Enables systematic comparison via configuration files

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 https://github.com/ModSSC/ModSSC. The framework is validated through controlled experiments reproducing established semi-supervised learning baselines across multiple data modalities.