Note: character.tar contains everything in this directory, as well as
      train and test sets.

1. TITLE: Artificial Character Database

2. SOURCES: 
                   Marco Botta
		   Dipartimento di Informatica
		   Universita` di Torino
		   Corso Svizzera 185
                   10149  Torino
                   ITALY
                   Tel. (+39)(11)7712002
                   Fax. (+39)(11)751603
                   email: botta@di.unito.it
                   July, 1992

3. PAST USAGE:
    (a) M. Botta, A. Giordana, L. Saitta: "Learning Fuzzy Concept Definitions",
	submitted to IEEE-Fuzzy Conference.

	We made three type of experiments: we run Smart+ by using default
	values for the parameter constants in the fuzzy definitions of the
	predicates, a local optimization algorithm and a genetic algorithm
	to automatically acquire parameter values. The local optimization
	algorithm and the GA are also described in:

	M. Botta, A. Giordana: "Learning Quantitative Feature in a Symbolic
	Environment", LNAI 542, 1991, pp. 296-305.

	The results obtained on this data sets are the following:

	Type of Optimization  Recognition Rate  Error Rate  Ambiguity Rate
	  
	  No OPT		  41.48%	   3.82%	 54.70%
	  Loacl OPT		  98.68%	   0.12%	  1.20%
	  Local+GA OPT		  99.70%	   0.0%		  0.30%

4. RELEVANT INFORMATION:

      This database has been artificially generated by using a first order
      theory which describes the structure of ten capitol letters of the
      English alphabet and a random choice theorem prover which accounts
      for etherogeneity in the instances. The capitol letters represented
      are the following: A, C, D, E, F, G, H, L, P, R.
      Each instance is structured and is described by a set of segments (lines)
      which resemble the way an automatic program would segment an image.
      Each instance is stored in a separate file whose format is the following:
              CLASS OBJNUM TYPE XX1 YY1 XX2 YY2 SIZE DIAG
      where CLASS is an integer number indicating the class as described below,
      OBJNUM is an integer identifier of a segment (starting from 0) in the
      instance and the remaining columns represent attribute values.
      For further details contact the author.

5. NUMBER OF INSTANCES:
      1000 instance (100 per class) as learning set.
      5000 instance (500 per class) as test set.

6. NUMBER OF ATTRIBUTES:
      Each segment in an instance is described by seven attributes, four of
      which are the most important, one is superflous, and the other two can be
      computed from the important ones, but are present for efficiency reasons.

7. ATTRIBUTE INFORMATION:

      TYPE: the first attribute describes the type of segment and is always
	    set to the string "line". Its C language type is char.

      XX1,YY1,XX2,YY2: these attributes contain the initial and final coordinates
		   of a segment in a cartesian plane. Their C language type is
		   int.

      SIZE: this is the length of a segment computed by using the geometric
	    distance between two points A(X1,Y1) and B(X2,Y2). Its C language
	    type is float.

      DIAG: this is the length of the diagonal of the smallest rectangle
	    which includes the picture of the character. The value of this
		attribute is the same in each object. Its C language
		type is float.

8. MISSING ATTRIBUTE VALUES: None

9. CLASS DISTRIBUTION:
        the class value (CLASS) can take ten different values. Each 
	letter belongs to only one of classes.


        CLASS	NAME  TRAINING  TESTING  TOTAL     

          1	  A 	100	  500	  600
          2	  C	100	  500	  600
          3	  D	100	  500	  600
          4	  E	100	  500	  600
          5	  F	100	  500	  600
          6	  G	100	  500	  600
          7	  H	100	  500	  600
          8	  L	100	  500	  600
          9	  P	100	  500	  600
          10	  R	100	  500	  600
          -----------------------------------------------------------
          TOTAL        1000      5000    6000