Datasets:

lifelonglab
/

CAD-MiniBooNe

pretty_name: CAD-MiniBooNe
license: cc-by-4.0
configs:
  - config_name: default
    data_files: data.csv
language:
  - en
task_categories:
  - tabular-classification
task_ids:
  - tabular-multi-class-classification
size_categories:
  - 10K<n<100K
tags:
  - anomaly-detection
  - continual-learning
  - continual-anomaly-detection
  - particle-identification
  - physics
  - tabular

CAD-MiniBooNe

Dataset Summary

CAD-MiniBooNe is a single-source continual anomaly detection benchmark scenario derived from the MiniBooNE Particle Identification dataset. It recasts the dataset's original binary classification task (distinguishing electron-neutrino "signal" events from muon-neutrino "background" events) as an anomaly detection problem and converts the tabular data into a sequence of concept-grouped tasks.

The dataset contains 66,116 samples, 5 tasks, and has a reported 72.47% anomaly ratio in the test set.

Intended Use

This dataset is intended for research on:

continual anomaly detection;
continual learning for tabular data;
robustness under distribution shift;
task ordering in continual-learning benchmarks;
forgetting and knowledge transfer across related concepts;
benchmarking anomaly detectors under sequential task exposure.

The intended use is machine learning research. As a physics benchmark, it carries no dual-use risk.

Dataset Source

MiniBooNE Particle Identification (UCI ML Repository, ID 199): https://archive.ics.uci.edu/dataset/199/miniboone+particle+identification

The original dataset originates from the MiniBooNE physics experiment and contains 130,065 events, each described by 50 real-valued particle-identification variables, labeled as electron-neutrino (signal) or muon-neutrino (background) events. Task boundaries in this benchmark are derived via spectral clustering over the original data, used as a proxy for concept drift.

Dataset Files

The repository contains the following files:

File	Description
`data.csv`	Main tabular dataset file.
`orderings.json`	Predefined task orderings for continual-learning evaluation.
`croissant.json`	Croissant metadata describing the dataset.

Dataset Structure

The main file is:

data.csv

The dataset contains task metadata, binary labels, and numerical particle-identification features.

Core Columns

Column	Type	Description
`task_id`	integer	Numeric identifier of the continual-learning task.
`task_name`	string	Name of the task, e.g. `miniboone_0`.
`task_split`	string	Split assignment for the row.
`label`	integer	Binary anomaly label. `0` denotes normal and `1` denotes anomalous.

Task Identifiers

The dataset contains the following task identifiers:

miniboone_0, miniboone_1, miniboone_2, miniboone_3, miniboone_4

Feature Columns

The remaining columns are the 50 real-valued particle-identification variables from the original MiniBooNE dataset, provided as feature_00 … feature_49.

For the complete schema, see croissant.json.

Task Orderings

The dataset provides six predefined orderings in orderings.json. These orderings define different continual-learning evaluation regimes over the same task set.

Ordering	Task sequence
`curriculum_asc`	`miniboone_2` → `miniboone_3` → `miniboone_0` → `miniboone_4` → `miniboone_1`
`curriculum_desc`	`miniboone_1` → `miniboone_4` → `miniboone_0` → `miniboone_3` → `miniboone_2`
`generalization_desc`	`miniboone_2` → `miniboone_0` → `miniboone_3` → `miniboone_4` → `miniboone_1`
`generalization_asc`	`miniboone_1` → `miniboone_4` → `miniboone_3` → `miniboone_0` → `miniboone_2`
`smooth_drift`	`miniboone_1` → `miniboone_4` → `miniboone_0` → `miniboone_3` → `miniboone_2`
`abrupt_drift`	`miniboone_0` → `miniboone_1` → `miniboone_2` → `miniboone_4` → `miniboone_3`

These orderings are intended to expose complementary continual-learning dynamics, including curriculum-like adaptation, generalization-oriented ordering, smooth drift, and abrupt drift.

Dataset Creation

The details of dataset creation can be found in our paper: link

Citation

When using the dataset, please cite:

@article{faber2026towards,
  title={Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios},
  author={Faber, Kamil and Smendowski, Mateusz and Corizzo, Roberto},
  journal={arXiv preprint arXiv:2607.18289},
  year={2026}
}

If you also use the underlying MiniBooNE data, please additionally cite:

Roe, B. (2010). MiniBooNE Particle Identification [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5QC87