Data
WaveformDatabaseGenerator

WaveformDatabaseGenerator

active ARFF Publicly available Visibility: public Uploaded 17-02-2016 by Hilda Fabiola Bernard
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Abstract: CART book's waveform domains Source: Original Owners: 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). Donor: David Aha Data Set Information: Notes: -- 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 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) Relevant Papers: Leo Breiman, Jerome H. Friedman, Adam Olshen, Jonathan Stone. "Classification and Regression Trees." 1984. [Web Link] Citation Request: Please refer to the Machine Learning Repository's citation policy

22 features

V1numeric519 unique values
0 missing
V2numeric546 unique values
0 missing
V3numeric608 unique values
0 missing
V4numeric684 unique values
0 missing
V5numeric766 unique values
0 missing
V6numeric808 unique values
0 missing
V7numeric878 unique values
0 missing
V8numeric791 unique values
0 missing
V9numeric762 unique values
0 missing
V10numeric725 unique values
0 missing
V11numeric770 unique values
0 missing
V12numeric720 unique values
0 missing
V13numeric762 unique values
0 missing
V14numeric804 unique values
0 missing
V15numeric885 unique values
0 missing
V16numeric816 unique values
0 missing
V17numeric774 unique values
0 missing
V18numeric681 unique values
0 missing
V19numeric600 unique values
0 missing
V20numeric571 unique values
0 missing
V21numeric525 unique values
0 missing
V22numeric3 unique values
0 missing

62 properties

5000
Number of instances (rows) of the dataset.
22
Number of attributes (columns) of the dataset.
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.
22
Number of numeric attributes.
0
Number of nominal attributes.
2.66
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
-0.24
Minimum skewness among attributes of the numeric type.
0
Percentage of nominal attributes.
0.2
Third quartile of skewness among attributes of the numeric type.
0.3
Maximum skewness among attributes of the numeric type.
0.82
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.75
Third quartile of standard deviation of attributes of the numeric type.
2.02
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.53
First quartile of kurtosis among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
0.67
First quartile of means among attributes of the numeric type.
-0.4
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
1.68
Mean of means among attributes of the numeric type.
-0.02
First quartile of skewness among attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
1.16
First quartile of standard deviation of attributes of the numeric type.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
-0.47
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.07
Mean skewness among attributes of the numeric type.
1.67
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
1.49
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.06
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.51
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
1.59
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
0.11
Maximum kurtosis among attributes of the numeric type.
-0.02
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
-0.13
Third quartile of kurtosis among attributes of the numeric type.
3.34
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

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