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breast-tissue

breast-tissue

active ARFF Publicly available Visibility: public Uploaded 21-05-2015 by Rafael Gomes 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

19 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.
0.95
Average class difference between consecutive instances.
0
Percentage of missing values.
0.09
Number of attributes divided by the number of instances.
90
Percentage of numeric attributes.
20.75
Percentage of instances belonging to the most frequent class.
10
Percentage of nominal attributes.
22
Number of instances belonging to the most frequent class.
13.21
Percentage of instances belonging to the least frequent class.
14
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.

13 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
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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