Data
breast-tissue

breast-tissue

active ARFF Publicly available Visibility: public Uploaded 21-05-2015 by Rafael G. Mantovani
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Author: JP Marques de Sá, J Jossinet Source: UCI Please cite: * Source: JP Marques de Sá, INEB-Instituto de Engenharia Biomédica, Porto, Portugal; e-mail: jpmdesa '@' gmail.com J Jossinet, inserm, Lyon, France * Data Set Information: Impedance measurements were made at the frequencies: 15.625, 31.25, 62.5, 125, 250, 500, 1000 KHz Impedance measurements of freshly excised breast tissue were made at the following frequencies: 15.625, 31.25, 62.5, 125, 250, 500, 1000 KHz. These measurements plotted in the (real, -imaginary) plane constitute the impedance spectrum from where the breast tissue features are computed. The dataset can be used for predicting the classification of either the original 6 classes or of 4 classes by merging together the fibro-adenoma, mastopathy and glandular classes whose discrimination is not important (they cannot be accurately discriminated anyway). * Attribute Information: I0 Impedivity (ohm) at zero frequency PA500 phase angle at 500 KHz HFS high-frequency slope of phase angle DA impedance distance between spectral ends AREA area under spectrum A/DA area normalized by DA MAX IP maximum of the spectrum DR distance between I0 and real part of the maximum frequency point P length of the spectral curve Class car(carcinoma), fad + mas + gla, con (connective), adi (adipose).

10 features

Class (target)nominal6 unique values
0 missing
V1numeric95 unique values
0 missing
V2numeric104 unique values
0 missing
V3numeric96 unique values
0 missing
V4numeric105 unique values
0 missing
V5numeric105 unique values
0 missing
V6numeric105 unique values
0 missing
V7numeric105 unique values
0 missing
V8numeric105 unique values
0 missing
V9numeric105 unique values
0 missing

62 properties

106
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
6
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.
9
Number of numeric attributes.
1
Number of nominal attributes.
-0.01
First quartile of skewness among attributes of the numeric type.
51.59
Mean of means among attributes of the numeric type.
28.81
First quartile of standard deviation of attributes of the numeric type.
0.95
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
2.56
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.
-1.19
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.09
Number of attributes divided by the number of instances.
6
Average number of distinct values among the attributes of the nominal type.
52.9
Second quartile (Median) of means 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.
0.02
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
20.75
Percentage of instances belonging to the most frequent class.
29.73
Mean standard deviation of attributes of the numeric type.
0.01
Second quartile (Median) of skewness among attributes of the numeric type.
22
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
30.32
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.22
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
-1.16
Maximum kurtosis among attributes of the numeric type.
46.03
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
-1.18
Third quartile of kurtosis among attributes of the numeric type.
53.4
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
90
Percentage of numeric attributes.
53.1
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
6
The minimal number of distinct values among attributes of the nominal type.
10
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
6
The maximum number of distinct values among attributes of the nominal type.
-0.02
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
0.03
Third quartile of skewness among attributes of the numeric type.
0.13
Maximum skewness among attributes of the numeric type.
27.57
Minimum standard deviation of attributes of the numeric type.
-1.21
First quartile of kurtosis among attributes of the numeric type.
30.41
Third quartile of standard deviation of attributes of the numeric type.
30.58
Maximum standard deviation of attributes of the numeric type.
13.21
Percentage of instances belonging to the least frequent class.
14
Number of instances belonging to the least frequent class.
50.05
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
-1.19
Mean kurtosis among attributes of the numeric type.

5 tasks

248 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
32 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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