Commit Inicial

data/tanveer/musk-1/clean1.info

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+1. Title: MUSK "Clean1" database
+
+2. Sources:
+   (a) Creators:  AI Group at Arris Pharmaceutical Corporation
+        contact:  David Chapman or Ajay Jain
+                  Arris Pharmaceutical Corporation
+                  385 Oyster Point Blvd.
+                  South San Francisco, CA 94080
+                  415-737-8600
+                  zvona@arris.com, jain@arris.com
+   (b) Donor:     Tom Dietterich
+                  Department of Computer Science
+                  Oregon State University
+                  Corvallis, OR 97331
+                  503-737-5559
+                  tgd@cs.orst.edu
+   (c) Date received: September 12, 1994
+
+3. Past Usage:
+   Dietterich, T. G., Lathrop, R. H., Lozano-Perez, T. (submitted)
+   Solving the multiple-instance problem with axis-parallel rectangles.
+   Submitted to Artificial Intelligence.
+
+   This paper compares several axis-parallel rectangle algorithms and
+   includes the following table:
+
+        Algorithm                TP FN FP TN  errs  %correct [CI]
+        iterated-discrim APR     42  5  2 43    7     92.4 [87.0--97.8]
+        GFS elim-kde APR         46  1  7 38    8     91.3 [85.5--97.1]
+        GFS elim-count APR       46  1  8 37    9     90.2 [84.2--96.3]
+        GFS all-positive APR     47  0 15 30   15     83.7 [76.2--91.2]
+        all-positive APR         36 11  7 38   18     80.4 [72.3--88.5]
+        backpropagation          45  2 21 24   23     75.0 [66.2--83.8]
+        C4.5 (pruned)            42  5 24 21   29     68.5 [40.9--61.3]
+
+        key: TP = true positives
+             FN = false negatives
+             FP = false positives
+             TN = true negatives
+             errs = errors = FN+FP
+             %correct = 10-fold cross-validation %correct.
+             CI = 95% confidence interval on proportion of correct
+             predictions.
+             For explanations of the various algorithms, see the
+             paper. 
+
+        C4.5 and backprop were applied ignoring the multiple instance
+        problem (see below) during training, but obeying it during
+        testing.  
+
+        This paper also gives more details on the construction of the
+        data set. 
+
+        This paper also describes an artificial generator that can
+        generate data sets with statistics and properties similar to
+        this one.
+
+4. Relevant Information:
+   This dataset describes a set of 92 molecules of which 47 are judged
+   by human experts to be musks and the remaining 45 molecules are
+   judged to be non-musks.  The goal is to learn to predict whether
+   new molecules will be musks or non-musks.  However, the 166 features
+   that describe these molecules depend upon the exact shape, or
+   conformation, of the molecule.  Because bonds can rotate, a single
+   molecule can adopt many different shapes.  To generate this data
+   set, the low-energy conformations of the molecules were generated
+   and then filtered to remove highly similar conformations. This left
+   476 conformations.  Then, a feature vector was extracted that
+   describes each conformation.
+
+   This many-to-one relationship between feature vectors and molecules
+   is called the "multiple instance problem".  When learning a
+   classifier for this data, the classifier should classify a molecule
+   as "musk" if ANY of its conformations is classified as a musk.  A
+   molecule should be classified as "non-musk" if NONE of its
+   conformations is classified as a musk.
+
+5. Number of Instances  476
+
+6. Number of Attributes 168 plus the class.
+
+7. For Each Attribute:
+   
+   Attribute:           Description:
+   molecule_name:       Symbolic name of each molecule.  Musks have names such
+                        as MUSK-188.  Non-musks have names such as
+                        NON-MUSK-jp13.
+   conformation_name:   Symbolic name of each conformation.  These
+                        have the format MOL_ISO+CONF, where MOL is the
+                        molecule number, ISO is the stereoisomer
+                        number (usually 1), and CONF is the
+                        conformation number. 
+   f1 through f162:     These are "distance features" along rays (see
+                        paper cited above).  The distances are
+                        measured in hundredths of Angstroms.  The
+                        distances may be negative or positive, since
+                        they are actually measured relative to an
+                        origin placed along each ray.  The origin was
+                        defined by a "consensus musk" surface that is
+                        no longer used.  Hence, any experiments with
+                        the data should treat these feature values as
+                        lying on an arbitrary continuous scale.  In
+                        particular, the algorithm should not make any
+                        use of the zero point or the sign of each
+                        feature value. 
+   f163:                This is the distance of the oxygen atom in the
+                        molecule to a designated point in 3-space.
+                        This is also called OXY-DIS.
+   f164:                OXY-X: X-displacement from the designated
+                        point.
+   f165:                OXY-Y: Y-displacement from the designated
+                        point.
+   f166:                OXY-Z: Z-displacement from the designated
+                        point. 
+   class:               0 => non-musk, 1 => musk
+
+   Please note that the molecule_name and conformation_name attributes
+   should not be used to predict the class.
+
+8. Missing Attribute Values: none.
+
+9. Class Distribution: 
+   Musks:     47
+   Non-musks: 45