Synthetic Classification Datasets for Metamorphic Testing of Machine Learning Models

Description This collection gathers multiple synthetic classification datasets generated using the make_classification function from the scikit-learn library.The datasets were designed to support the training and evaluation of machine learning classification models under a wide variety of controlled conditions, and were specifically used to assess model behavior through multiple metamorphic relations. The collection includes datasets with varying: numbers of classes (from 2 to 5); numbers of samples and features; noise levels (no noise, 10% and 60% of noisy samples); distribution (homogeneous, and heterogeneous). Dataset Structure Each dataset contains at least the following files: original.csv: Coordinates of the data points (feature values). class.csv: Class labels associated with each data point. config.csv: Metadata indicating which attributes are contextual and which are behavioral, as used in the metamorphic testing framework. Some datasets additionally include the following directories: MR_points/: CSV files containing points generated for metamorphic relations applied to the full dataset, including deliberately generated outliers. splited_convexes/: CSV files containing points produced for three convex-based metamorphic relations, applied after splitting the original dataset into smaller subsets. Two splitting strategies are provided: chained and independent. Special Datasets 4-classes_1000x2A specific 2D dataset composed of four classes arranged into four symmetrically placed clusters.The clusters contain identical point distributions, modulo geometric transformations applied to enforce symmetry. Small intersection-focused datasets: 3_classes_close 3_classes_far 5_classes_4_superposed 5_classes_close 5_classes_far These datasets were intentionally constructed with manually positioned clusters in order to produce intersections of different shapes and complexities, enabling fine-grained analysis of classifier behavior in overlapping decision regions. Related Publications This dataset collection has been used in previously published research as well as in a doctoral dissertation, and continues to support ongoing work. Conference paper: Colonval, J., & Bouquet, F. (2025). New Convex-Based Metamorphic Relations and Large-Scale Machine Learning Model Evaluation. In Lecture Notes in Computer Science, Springer.DOI: https://doi.org/10.1007/978-3-032-05188-2_23 Doctoral thesis: Colonval, J. (2024). Validation de composants d'intelligence artificielle issus d'apprentissages sur la base de détection d'aberration et de tests métamorphiques. PhD Thesis.Available at: https://theses.hal.science/tel-05016629v1/ Ongoing work:An additional research article based on these datasets is currently under preparation.