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skin-segmentation

skin-segmentation

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Author: Rajen Bhatt, Abhinav Dhall Source: UCI Please cite: Rajen Bhatt, Abhinav Dhall, 'Skin Segmentation Dataset', UCI Machine Learning Repository * Title: Skin Segmentation Data Set * Abstract: The Skin Segmentation dataset is constructed over B, G, R color space. Skin and Nonskin dataset is generated using skin textures from face images of diversity of age, gender, and race people. * Source: Rajen Bhatt, Abhinav Dhall, rajen.bhatt '@' gmail.com, IIT Delhi. * Data Set Information: The skin dataset is collected by randomly sampling B,G,R values from face images of various age groups (young, middle, and old), race groups (white, black, and asian), and genders obtained from FERET database and PAL database. Total learning sample size is 245057; out of which 50859 is the skin samples and 194198 is non-skin samples. Color FERET Image Database: [Web Link], PAL Face Database from Productive Aging Laboratory, The University of Texas at Dallas: [Web Link]. * Attribute Information: This dataset is of the dimension 245057 * 4 where first three columns are B,G,R (x1,x2, and x3 features) values and fourth column is of the class labels (decision variable y). * Relevant Papers: 1. Rajen B. Bhatt, Gaurav Sharma, Abhinav Dhall, Santanu Chaudhury, “Efficient skin region segmentation using low complexity fuzzy decision tree model”, IEEE-INDICON 2009, Dec 16-18, Ahmedabad, India, pp. 1-4. 2. Abhinav Dhall, Gaurav Sharma, Rajen Bhatt, Ghulam Mohiuddin Khan, “Adaptive Digital Makeup”, in Proc. of International Symposium on Visual Computing (ISVC) 2009, Nov. 30 – Dec. 02, Las Vegas, Nevada, USA, Lecture Notes in Computer Science, Vol. 5876, pp. 728-736.

4 features

Class (target)nominal2 unique values
0 missing
V1numeric256 unique values
0 missing
V2numeric256 unique values
0 missing
V3numeric256 unique values
0 missing

62 properties

245057
Number of instances (rows) of the dataset.
4
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.
3
Number of numeric attributes.
1
Number of nominal attributes.
25
Percentage of binary attributes.
62.26
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.92
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
-0.6
Maximum kurtosis among attributes of the numeric type.
123.18
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
-0.6
Third quartile of kurtosis among attributes of the numeric type.
132.51
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
75
Percentage of numeric attributes.
132.51
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
25
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-0.62
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
-0.04
Third quartile of skewness among attributes of the numeric type.
-0.04
Maximum skewness among attributes of the numeric type.
59.94
Minimum standard deviation of attributes of the numeric type.
-0.92
First quartile of kurtosis among attributes of the numeric type.
72.56
Third quartile of standard deviation of attributes of the numeric type.
72.56
Maximum standard deviation of attributes of the numeric type.
20.75
Percentage of instances belonging to the least frequent class.
123.18
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.
50859
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.79
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
-0.62
First quartile of skewness among attributes of the numeric type.
126.92
Mean of means among attributes of the numeric type.
59.94
First quartile of standard deviation of attributes of the numeric type.
1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.74
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.
-0.86
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
125.07
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.
-0.33
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
79.25
Percentage of instances belonging to the most frequent class.
64.92
Mean standard deviation of attributes of the numeric type.
-0.34
Second quartile (Median) of skewness among attributes of the numeric type.
194198
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

5 tasks

15 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
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