BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 90 insertions, 0 deletions

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+Description of the Dataset:
+
+THIS CREDIT DATA ORIGINATES FROM QUINLAN (see below).   
+
+1. Title: Australian Credit Approval
+
+2. Sources: 
+    (confidential)
+    Submitted by quinlan@cs.su.oz.au
+
+3.  Past Usage:
+
+    See Quinlan,
+    * "Simplifying decision trees", Int J Man-Machine Studies 27,
+      Dec 1987, pp. 221-234.
+    * "C4.5: Programs for Machine Learning", Morgan Kaufmann, Oct 1992
+  
+4.  Relevant Information:
+
+    This file concerns credit card applications.  All attribute names
+    and values have been changed to meaningless symbols to protect
+    confidentiality of the data.
+  
+    This dataset is interesting because there is a good mix of
+    attributes -- continuous, nominal with small numbers of
+    values, and nominal with larger numbers of values.  There
+    are also a few missing values.
+  
+5.  Number of Instances: 690
+
+6.  Number of Attributes: 14 + class attribute
+
+7.  Attribute Information:   THERE ARE 6 NUMERICAL AND 8 CATEGORICAL ATTRIBUTES.
+ 
+                             THE LABELS HAVE BEEN CHANGED FOR THE CONVENIENCE
+                             OF THE STATISTICAL ALGORITHMS.   FOR EXAMPLE,
+                             ATTRIBUTE 4 ORIGINALLY HAD 3 LABELS p,g,gg AND
+                             THESE HAVE BEEN CHANGED TO LABELS 1,2,3.
+                             
+
+    A1:	0,1    CATEGORICAL
+        a,b
+    A2:	continuous.
+    A3:	continuous.
+    A4:	1,2,3         CATEGORICAL
+        p,g,gg
+    A5:  1, 2,3,4,5, 6,7,8,9,10,11,12,13,14    CATEGORICAL
+         ff,d,i,k,j,aa,m,c,w, e, q, r,cc, x 
+         
+    A6:	 1, 2,3, 4,5,6,7,8,9    CATEGORICAL
+        ff,dd,j,bb,v,n,o,h,z 
+
+    A7:	continuous.
+    A8:	1, 0       CATEGORICAL
+        t, f.
+    A9: 1, 0	    CATEGORICAL
+        t, f.
+    A10:	continuous.
+    A11:  1, 0	    CATEGORICAL
+          t, f.
+    A12:    1, 2, 3    CATEGORICAL
+            s, g, p 
+    A13:	continuous.
+    A14:	continuous.
+    A15:   1,2
+           +,-         (class attribute)
+
+8.  Missing Attribute Values:
+    37 cases (5%) HAD one or more missing values.  The missing
+    values from particular attributes WERE:
+
+    A1:  12
+    A2:  12
+    A4:   6
+    A5:   6
+    A6:   9
+    A7:   9
+    A14: 13
+    
+    THESE WERE REPLACED BY THE MODE OF THE ATTRIBUTE (CATEGORICAL)
+                               MEAN OF THE ATTRIBUTE (CONTINUOUS)
+                           
+9.  Class Distribution
+  
+    +: 307 (44.5%)    CLASS 2
+    -: 383 (55.5%)    CLASS 1
+
+
+10.  There is no cost matrix.
+