README.md

metadata

license: mit
task_categories:
  - tabular-classification
language:
  - en
tags:
  - synthetic
  - sparse-learning
  - classification
size_categories:
  - 100K<n<1M

is_sparse/sparse5d

Dataset Description

This is a synthetic 5-dimensional classification dataset designed for sparse learning research. The dataset contains 3 classes and is specifically designed to have sparse optimal representations, where only a subset of features are informative for the classification task.

Dataset Summary

• Variant: sparse5d
• Features: 5 continuous features
• Classes: 3
• Entropy(Y): 1.4855
• Mutual Information (joint): 1.1819
• Maximum Achievable Accuracy: 0.8967

Dataset Structure

Data Instances

Each instance consists of:

• data: A 5-dimensional feature vector (float32)
• label: An integer class label (0, 1, or 2)

Data Splits

Split	Number of Instances
Train	Variable (see below)
Test	Variable (see below)

Dataset Creation

This dataset was synthetically generated for research on sparse learning and optimal feature selection. The mutual information values between feature subsets and labels are provided in the metadata.

Mutual Information Structure

The dataset includes ground-truth mutual information values for various feature subsets, enabling:

• Feature importance analysis
• Information-theoretic learning algorithms
• Benchmarking of MI estimation methods

Key MI values:

• joint: 1.1819
• 1: 0.3273
• 1-2: 0.3273
• 1-2-3: 0.6634
• 1-2-3-4: 0.6634
• 1-2-3-4-5: 1.1819
• 1-2-3-5: 1.1819
• 1-2-4: 0.3273
• 1-2-4-5: 1.0492
• 1-2-5: 1.0492

Citation

If you use this dataset, please cite the associated research paper (to be added).

License

MIT License