Ionosphere

Donated on 12/31/1988

Classification of radar returns from the ionosphere

Dataset Characteristics

Multivariate

Subject Area

Physics and Chemistry

Associated Tasks

Classification

Feature Type

Integer, Real

# Instances

351

# Features

34

Dataset Information

Additional Information

This radar data was collected by a system in Goose Bay, Labrador. This system consists of a phased array of 16 high-frequency antennas with a total transmitted power on the order of 6.4 kilowatts. See the paper for more details. The targets were free electrons in the ionosphere. "Good" radar returns are those showing evidence of some type of structure in the ionosphere. "Bad" returns are those that do not; their signals pass through the ionosphere. Received signals were processed using an autocorrelation function whose arguments are the time of a pulse and the pulse number. There were 17 pulse numbers for the Goose Bay system. Instances in this databse are described by 2 attributes per pulse number, corresponding to the complex values returned by the function resulting from the complex electromagnetic signal.

Has Missing Values?

No

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
Attribute1FeatureContinuousno
Attribute2FeatureContinuousno
Attribute3FeatureContinuousno
Attribute4FeatureContinuousno
Attribute5FeatureContinuousno
Attribute6FeatureContinuousno
Attribute7FeatureContinuousno
Attribute8FeatureContinuousno
Attribute9FeatureContinuousno
Attribute10FeatureContinuousno

0 to 10 of 35

Additional Variable Information

-- All 34 are continuous -- The 35th attribute is either "good" or "bad" according to the definition summarized above. This is a binary classification task.

Baseline Model Performance

Dataset Files

FileSize
ionosphere.data74.7 KB
ionosphere.names3 KB
Index123 Bytes

Papers Citing this Dataset

Support Feature Machines

By Tomasz Maszczyk, Wlodzislaw Duch. 2019

Published in World Congress on Computational Intelligence, IEEE Press, pp. 3852-3859, 2010.

Differentially Private Precision Matrix Estimation

By Wenqing Su, Xiao Guo, Hai Zhang. 2019

Published in ArXiv.

Enumeration of Distinct Support Vectors for Interactive Decision Making

By Kentaro Kanamori, Satoshi Hara, Masakazu Ishihata, Hiroki Arimura. 2019

Published in ArXiv.

Meta-learning: searching in the model space

By Wlodzislaw Duch, Karol Grudzi'nsk. 2018

Published in Proceedings of the International Conference on Neural Information Processing, Shanghai, 2001, Vol. I, pp. 235-240.

Extreme Learning Machine with Local Connections

By Feng Li, Sibo Yang, Huanhuan Huang, Wei Wu. 2018

Published in ArXiv.

0 to 5 of 47

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Download (29.3 KB)
47 citations
26776 views

Creators

V. Sigillito

S. Wing

L. Hutton

K. Baker

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