source: proiecte/Parallel-DT/R8/Data/monk3.names @ 26

|            *******************************************************
|              ***************************************************
|                 **********************************************
|                   ******************************************
|                     T H E   M O N K' S    P R O B L E M S
|                   ******************************************
|                 **********************************************
|              ***************************************************
|            *******************************************************
|  
|  
|  
|  The MONK's problems are a collection of three binary classification
|  problems over a six-attribute discrete domain. Each training/test data
|  is of the form
|  
|    <name>: <value1> <value2> <value3> <value4> <value5> <value6> -> <class>
|  
|  where <name> is an ASCII-string, <value n> represents the value of
|  attribute # n, and <class> is either 0 or 1, depending on the class
|  this example belongs to. The attributes may take the following values:
|  
|       attribute#1 :   {1, 2, 3}
|       attribute#2 :   {1, 2, 3}
|       attribute#3 :   {1, 2}
|       attribute#4 :   {1, 2, 3}
|       attribute#5 :   {1, 2, 3, 4}
|       attribute#6 :   {1, 2}  
|  
|  Thus, the six attributes span a space of 432=3x3x2x3x4x2 examples.
|  
|  
|  
|  /*********************************************************************\
|  ***********************************************************************
|  \*********************************************************************/
|  
|  
|  
|  The "true" concepts underlying each MONK's problem are given by:
|  
|        MONK-1: (attribute_1 = attribute_2) or (attribute_5 = 1)
|  
|        MONK-2: (attribute_n = 1) for EXACTLY TWO choices of n (in {1,2,...,6})
|  
|        MONK-3: (attribute_5  = 3 and attribute_4  = 1) or
|                (attribute_5 != 4 and attribute_2 != 3)
|  
|  (with "!=" denoting inequality).
|  MONK-3 has 5% additional noise (misclassifications) in the training set.
|  
|  
|  
|  /*********************************************************************\
|  ***********************************************************************
|  \*********************************************************************/

0, 1                            |classes

attribute#1 :   1, 2, 3
attribute#2 :   1, 2, 3
attribute#3 :   1, 2
attribute#4 :   1, 2, 3
attribute#5 :   1, 2, 3, 4
attribute#6 :   1, 2