Data
ionosphere

ionosphere

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
3 likes downloaded by 26 people , 40 total downloads 0 issues 0 downvotes
  • mythbusting_1 study_1 study_123 study_15 study_20 study_29 study_30 study_41 study_50 study_52 study_7 study_88 uci
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Space Physics Group, Applied Physics Laboratory, Johns Hopkins University. Donated by Vince Sigillito. Source: [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/ionosphere) Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) Johns Hopkins University Ionosphere database 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. ### Attribute information 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 database are described by 2 attributes per pulse number, corresponding to the complex values returned by the function resulting from the complex electromagnetic signal. The targets were free electrons in the ionosphere. "Good" (g) radar returns are those showing evidence of some type of structure in the ionosphere. "Bad" (b) returns are those that do not; their signals pass through the ionosphere. ### 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.

35 features

class (target)nominal2 unique values
0 missing
a01numeric2 unique values
0 missing
a02numeric1 unique values
0 missing
a03numeric219 unique values
0 missing
a04numeric269 unique values
0 missing
a05numeric204 unique values
0 missing
a06numeric259 unique values
0 missing
a07numeric231 unique values
0 missing
a08numeric260 unique values
0 missing
a09numeric244 unique values
0 missing
a10numeric267 unique values
0 missing
a11numeric246 unique values
0 missing
a12numeric269 unique values
0 missing
a13numeric238 unique values
0 missing
a14numeric266 unique values
0 missing
a15numeric234 unique values
0 missing
a16numeric270 unique values
0 missing
a17numeric254 unique values
0 missing
a18numeric280 unique values
0 missing
a19numeric254 unique values
0 missing
a20numeric266 unique values
0 missing
a21numeric248 unique values
0 missing
a22numeric265 unique values
0 missing
a23numeric248 unique values
0 missing
a24numeric264 unique values
0 missing
a25numeric256 unique values
0 missing
a26numeric273 unique values
0 missing
a27numeric256 unique values
0 missing
a28numeric281 unique values
0 missing
a29numeric244 unique values
0 missing
a30numeric266 unique values
0 missing
a31numeric243 unique values
0 missing
a32numeric263 unique values
0 missing
a33numeric245 unique values
0 missing
a34numeric263 unique values
0 missing

107 properties

351
Number of instances (rows) of the dataset.
35
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
34
Number of numeric attributes.
1
Number of nominal attributes.
0.52
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.1
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
2.86
Percentage of binary attributes.
Third quartile of entropy among attributes.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
-2.53
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
0.62
Third quartile of kurtosis among attributes of the numeric type.
0.28
Average class difference between consecutive instances.
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.11
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.4
Third quartile of means among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.65
Maximum standard deviation of attributes of the numeric type.
35.9
Percentage of instances belonging to the least frequent class.
97.14
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
126
Number of instances belonging to the least frequent class.
2.86
Percentage of nominal attributes.
0.02
Third quartile of skewness among attributes of the numeric type.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.36
Mean kurtosis among attributes of the numeric type.
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.57
Third quartile of standard deviation of attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.25
Mean of means among attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.21
First quartile of kurtosis among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.01
First quartile of means among attributes of the numeric type.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
-0.88
First quartile of skewness among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0.58
Mean skewness among attributes of the numeric type.
0.49
First quartile of standard deviation of attributes of the numeric type.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.15
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
64.1
Percentage of instances belonging to the most frequent class.
0.51
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.94
Entropy of the target attribute values.
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
225
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.01
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.67
Minimum kurtosis among attributes of the numeric type.
0.26
Second quartile (Median) of means among attributes of the numeric type.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.17
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
4.44
Maximum kurtosis among attributes of the numeric type.
-0.07
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
-0.61
Second quartile (Median) of skewness among attributes of the numeric type.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.89
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.

17 tasks

1200 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
366 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
357 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
207 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
211 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
87 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task