Dataset60: High-dimensional Datasets for Feature Selection

Dataset60 comprises 60 high-dimensional datasets sourced from open repositories, systematically curated and formatted to establish a new benchmark for evaluating feature selection algorithms.

A summary of each dataset, including its name, number of samples (n_sample), features (n_feature), classes (n_class), and quantity/proportion for each label (label_distribution), is available in the "summary_dataset60.csv" file.

Datasets are obtained from various sources:

https://jundongl.github.io/scikit-feature/datasets.html

https://zenodo.org/records/2709491

https://archive.ics.uci.edu/datasets

https://data.mendeley.com/datasets/fhx5zgx2zj/1

https://ckzixf.github.io/dataset.html

The preprocessing steps include:

Removing an index/id column if present.

Encoding labels numerically, starting from 0 (0/1/2/…).

Re-naming headers to f{feature_index} & label (f1, f2, …, label).

Addressing missing values: Following instructions in the README/Dataset Info if available; otherwise, filling missing values with 0.

Important note: The data has NOT undergone standardization.