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README.md

@@ -1,30 +1,83 @@
 ---
-dataset_info:
-  features:
-  - name: data
-    list: float32
-    length: 5
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': '0'
-          '1': '1'
-          '2': '2'
-  splits:
-  - name: train
-    num_bytes: 22400000
-    num_examples: 800000
-  - name: test
-    num_bytes: 5600000
-    num_examples: 200000
-  download_size: 21446572
-  dataset_size: 28000000
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+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
+
+### Supported Tasks
+
+- `tabular-classification`: The dataset can be used to train models for multi-class classification tasks.
+- Sparse learning research: Study the effectiveness of feature selection and sparse representation learning.
+- Mutual information estimation: Benchmark MI estimation algorithms using the provided ground-truth MI values.
+
+## 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