Ionosphere Data Set Download: Data Folder, Data Set Description Abstract: Classification of radar returns from the ionosphere

Source:

Donor:

Vince Sigillito (vgs '@' aplcen.apl.jhu.edu)

Source:

Space Physics Group
Applied Physics Laboratory
Johns Hopkins University
Johns Hopkins Road
Laurel, MD 20723

Data Set 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.

Attribute 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.

Relevant Papers:

Sigillito, V. G., Wing, S. P., Hutton, L. V., \& Baker, K. B. (1989). Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Technical Digest, 10, 262-266.
[Web Link]

Papers That Cite This Data Set¹:

Hyunsoo Kim and Se Hyun Park. Data Reduction in Support Vector Machines by a Kernelized Ionic Interaction Model. SDM. 2004. [View Context].

Glenn Fung and M. Murat Dundar and Jinbo Bi and Bharat Rao. A fast iterative algorithm for fisher discriminant using heterogeneous kernels. ICML. 2004. [View Context].

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Zhi-Hua Zhou and Yuan Jiang. NeC4.5: Neural Ensemble Based C4.5. IEEE Trans. Knowl. Data Eng, 16. 2004. [View Context].

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Marina Skurichina and Ludmila Kuncheva and Robert P W Duin. Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy. Multiple Classifier Systems. 2002. [View Context].

Robert Burbidge and Matthew Trotter and Bernard F. Buxton and Sean B. Holden. STAR - Sparsity through Automated Rejection. IWANN (1). 2001. [View Context].

P. S and Bradley K. P and Bennett A. Demiriz. Constrained K-Means Clustering. Microsoft Research Dept. of Mathematical Sciences One Microsoft Way Dept. of Decision Sciences and Eng. Sys. 2000. [View Context].

Juan J. Rodr##guez and Carlos J. Alonso and Henrik Bostrom. Boosting Interval Based Literals. 2000. [View Context].

Colin Campbell and Nello Cristianini and Alex J. Smola. Query Learning with Large Margin Classifiers. ICML. 2000. [View Context].

Marina Skurichina and Robert P W Duin. Boosting in Linear Discriminant Analysis. Multiple Classifier Systems. 2000. [View Context].

Lorne Mason and Peter L. Bartlett and Jonathan Baxter. Improved Generalization Through Explicit Optimization of Margins. Machine Learning, 38. 2000. [View Context].

Justin Bradley and Kristin P. Bennett and Bennett A. Demiriz. Constrained K-Means Clustering. Microsoft Research Dept. of Mathematical Sciences One Microsoft Way Dept. of Decision Sciences and Eng. Sys. 2000. [View Context].

Jennifer G. Dy and Carla Brodley. Feature Subset Selection and Order Identification for Unsupervised Learning. ICML. 2000. [View Context].

Art B. Owen. Tubular neighbors for regression and classification. Stanford University. 1999. [View Context].

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Lorne Mason and Jonathan Baxter and Peter L. Bartlett and Marcus Frean. Boosting Algorithms as Gradient Descent. NIPS. 1999. [View Context].

Kai Ming Ting and Ian H. Witten. Issues in Stacked Generalization. J. Artif. Intell. Res. (JAIR, 10. 1999. [View Context].

Stephen D. Bay. Nearest neighbor classification from multiple feature subsets. Intell. Data Anal, 3. 1999. [View Context].

Stavros J. Perantonis and Vassilis Virvilis. Input Feature Extraction for Multilayered Perceptrons Using Supervised Principal Component Analysis. Neural Processing Letters, 10. 1999. [View Context].

David M J Tax and Robert P W Duin. Support vector domain description. Pattern Recognition Letters, 20. 1999. [View Context].

Robert E. Schapire and Yoav Freund and Peter Bartlett and Wee Sun Lee. The Annals of Statistics, to appear. Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods. AT&T Labs. 1998. [View Context].

Lorne Mason and Peter L. Bartlett and Jonathan Baxter. Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS. 1998. [View Context].

Richard Maclin. Boosting Classifiers Regionally. AAAI/IAAI. 1998. [View Context].

Kristin P. Bennett and Erin J. Bredensteiner. A Parametric Optimization Method for Machine Learning. INFORMS Journal on Computing, 9. 1997. [View Context].

Aynur Akkus and H. Altay Güvenir. K Nearest Neighbor Classification on Feature Projections. ICML. 1996. [View Context].

Alain Rakotomamonjy. Leave-One-Out errors in Bipartite Ranking SVM. PSI CNRS FRE2645 INSA de Rouen Avenue de l'universite. [View Context].

Wl/odzisl/aw Duch and Karol Grudzinski. Meta-learning: searching in the model space. Department of Computer Methods, Nicholas Copernicus University. [View Context].

Federico Divina and Elena Marchiori. Knowledge-Based Evolutionary Search for Inductive Concept Learning. Vrije Universiteit of Amsterdam. [View Context].

Charles Campbell and Nello Cristianini. Simple Learning Algorithms for Training Support Vector Machines. Dept. of Engineering Mathematics. [View Context].

K. A. J Doherty and Rolf Adams and Neil Davey. Unsupervised Learning with Normalised Data and Non-Euclidean Norms. University of Hertfordshire. [View Context].

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Wl/odzisl/aw Duch and Karol Grudzinski and Geerd H. F Diercksen. Minimal distance neural methods. Department of Computer Methods, Nicholas Copernicus University. [View Context].

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