metadata

license: cc-by-4.0

Ionosphere Signals Dataset

Overview

This dataset contains tabular data for classifying radar returns from the ionosphere. Each sample is stored in a separate text file, with features space-separated on a single line. The dataset is structured to be compatible with Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application or API.

Dataset Structure

The dataset is organized into the following structure:

Ionosphere-Signals/ train_data/ class_good/ sample_0.txt sample_1.txt ... class_bad/ sample_0.txt sample_1.txt ... test_data/ class_good/ sample_0.txt sample_1.txt ... class_bad/ sample_0.txt sample_1.txt ...

Note: All text file names must be unique across all class folders.

Features

Tabular Data: Each text file contains space-separated values representing the features of a sample.
Classes: There are two classes, each represented by a separate folder based on the type of radar return.

Usage

Here is an example of how to load the dataset using PrismRCL:

C:\PrismRCL\PrismRCL.exe chisquared rclticks=10 boxdown=0 data=C:\path\to\Ionosphere-Signals\train_data testdata=C:\path\to\Ionosphere-Signals\test_data savemodel=C:\path\to\models\mymodel.classify log=C:\path\to\log_files stopwhendone

Explanation:

C:\PrismRCL\PrismRCL.exe: classification application
chisquared: training evaluation method
rclticks=10: RCL training parameter
boxdown=0: RCL training parameter
data=C:\path\to\Ionosphere-Signals\train_data: path to training data
testdata=C:\path\to\Ionosphere-Signals\test_data: path to testing data
savemodel=C:\path\to\models\mymodel.classify: path to save resulting model
log=C:\path\to\log_files: path to logfiles
stopwhendone: ends the PrismRCL session when training is done

License

This dataset is licensed under the Creative Commons Attribution 4.0 International License. See the LICENSE file for more details.

Original Source

This dataset was originally sourced from the UCI Machine Learning Repository. Please cite the original source if you use this dataset in your research or applications.

Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [https://archive.ics.uci.edu/dataset/52/ionosphere]. Irvine, CA: University of California, School of Information and Computer Science.

Additional Information

The data values have been prepared to ensure compatibility with PrismRCL. No normalization is required as of version 2.4.0.