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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 '@', 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

19 properties

Number of instances (rows) of the dataset.
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
Number of missing values in the dataset.
Number of instances with at least one value missing.
Number of numeric attributes.
Number of nominal attributes.
Number of attributes divided by the number of instances.
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
Number of binary attributes.
Percentage of binary attributes.
Percentage of instances having missing values.
Average class difference between consecutive instances.
Percentage of missing values.

5 tasks

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