Data
waveform-5000

waveform-5000

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: 1. Title: Waveform Database Generator (written in C) 2. Source: (a) Breiman,L., Friedman,J.H., Olshen,R.A., & Stone,C.J. (1984). Classification and Regression Trees. Wadsworth International Group: Belmont, California. (see pages 43-49). (b) Donor: David Aha (c) Date: 11/10/1988 3. Past Usage: 1. CART book (above): -- Optimal Bayes classification rate: 86% accuracy -- CART decision tree algorithm: 72% -- Nearest Neighbor Algorithm: 78% -- 300 training and 5000 test instances 4. Relevant Information: -- 3 classes of waves -- 21 attributes, all of which include noise -- See the book for details (49-55, 169) -- waveform.data.Z contains 5000 instances 5. Number of Instances: chosen by user 6. Number of Attributes: -- 21 attributes with continuous values between 0 and 6 7. Attribute Information: -- Each class is generated from a combination of 2 of 3 "base" waves -- Each instance is generated f added noise (mean 0, variance 1) in each attribute -- See the book for details (49-55, 169) 8. Missing Attribute Values: none 9. Class Distribution: 33% for each of 3 classes

41 features

class (target)nominal3 unique values
0 missing
x1numeric530 unique values
0 missing
x2numeric542 unique values
0 missing
x3numeric615 unique values
0 missing
x4numeric692 unique values
0 missing
x5numeric773 unique values
0 missing
x6numeric816 unique values
0 missing
x7numeric891 unique values
0 missing
x8numeric803 unique values
0 missing
x9numeric765 unique values
0 missing
x10numeric714 unique values
0 missing
x11numeric749 unique values
0 missing
x12numeric730 unique values
0 missing
x13numeric769 unique values
0 missing
x14numeric794 unique values
0 missing
x15numeric887 unique values
0 missing
x16numeric817 unique values
0 missing
x17numeric755 unique values
0 missing
x18numeric682 unique values
0 missing
x19numeric606 unique values
0 missing
x20numeric555 unique values
0 missing
x21numeric532 unique values
0 missing
x22numeric528 unique values
0 missing
x23numeric517 unique values
0 missing
x24numeric525 unique values
0 missing
x25numeric528 unique values
0 missing
x26numeric524 unique values
0 missing
x27numeric526 unique values
0 missing
x28numeric535 unique values
0 missing
x29numeric528 unique values
0 missing
x30numeric526 unique values
0 missing
x31numeric536 unique values
0 missing
x32numeric531 unique values
0 missing
x33numeric529 unique values
0 missing
x34numeric523 unique values
0 missing
x35numeric538 unique values
0 missing
x36numeric516 unique values
0 missing
x37numeric512 unique values
0 missing
x38numeric521 unique values
0 missing
x39numeric532 unique values
0 missing
x40numeric531 unique values
0 missing

107 properties

5000
Number of instances (rows) of the dataset.
41
Number of attributes (columns) of the dataset.
3
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.
40
Number of numeric attributes.
1
Number of nominal attributes.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3
Average number of distinct values among the attributes of the nominal type.
-0.02
First quartile of skewness among attributes of the numeric type.
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.85
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
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.04
Mean skewness among attributes of the numeric type.
1
First quartile of standard deviation of attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.23
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
0.65
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
0.26
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
33.84
Percentage of instances belonging to the most frequent class.
1.27
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.58
Entropy of the target attribute values.
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1692
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.05
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.68
Minimum kurtosis among attributes of the numeric type.
0.03
Second quartile (Median) of means among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.44
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.15
Maximum kurtosis among attributes of the numeric type.
-0.03
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
3.32
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.02
Second quartile (Median) of skewness among attributes of the numeric type.
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
3
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
1.01
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.29
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.
3
The maximum number of distinct values among attributes of the nominal type.
-0.27
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.27
Maximum skewness among attributes of the numeric type.
0.98
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.02
Third quartile of kurtosis among attributes of the numeric type.
0.33
Average class difference between consecutive instances.
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.03
Maximum standard deviation of attributes of the numeric type.
33.06
Percentage of instances belonging to the least frequent class.
97.56
Percentage of numeric attributes.
2
Third quartile of means among attributes of the numeric type.
0.85
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
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
1653
Number of instances belonging to the least frequent class.
2.44
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.23
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
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.2
Mean kurtosis among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.08
Third quartile of skewness among attributes of the numeric type.
0.65
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
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.9
Mean of means among attributes of the numeric type.
0.2
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.48
First quartile of kurtosis among attributes of the numeric type.
1.66
Third quartile of standard deviation of attributes of the numeric type.
0.85
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
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0
First quartile of means among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.23
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
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.83
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.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.65
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

11 tasks

0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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