Dataset60: High-dimensional Datasets for Feature Selection

Dataset60 comprises 62 high-dimensional datasets sourced from open repositories (in version 2, two new datasets have been included), 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. (The datasets in "summary_dataset60.csv" are arranged in ascending order based on the number of features (n_feature), starting from the dataset with the lowest number of features and progressing to the dataset with the highest number.) AII 62 raw 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. If you need access to datasets processed with standardization (STD), they are available in the Dataset60_std.zip file. In this collection, the 62 datasets are identified by the "_std" suffix, indicating that they have undergone standardization.