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