_PROBLEM CoEPrA-2006_Classification_002

_GROUP_NAME Matt Segall

_GROUP_MEMBERS
Joelle Gola
Olga Obrezanova 
Matt Segall

_ADDRESS
Inpharmatica
127 Cambridge Science Park
Milton Road, Cambridge, CB4 0GD, UK
tel. +44(0)1223 706177
e-mail: o.obrezanova@inpharmatica.co.uk

_MODELING_PROCEDURE
1.Calculated one additional descriptor - Molecular weight. 
(for example, see http://www.expasy.org/tools/protparam.html)

2. Descriptor pre-selection. 
  Descriptors with low standard deviation, low occurence and highly 
  correlated were filtered out. 
  Excluded descriptors
- with standard deviation <  5.0E-04
- with occurence < 0.5%
- with correlation coefficient >= 0.9 (only one of a pair left). 

After filtering 1289 descriptors remained. 
 
3. Modelling technique. 
Decision Tree using C4.5 algorithm.
Gain ratio criterion is used to select descriptors.
Stopping rules used:  minimum number of cases on a tree leaf is  2,
upper limit for relative frequency of majority class on a node = 0.85.
 
4. Validation.
Cross-Validation procedure (5 groups) was used to obtain an estimate 
for the misclassification rate on unseen set.

5. Final model and statistics.
Calibration set: 76 cases, 1289 descriptors.

 Decision tree on calibration set:

 |Desc_02349 <= 0.84, 
 | |Desc_04786 <= -0.38, class=1  (9/0) 
 | |Desc_04786 >  -0.38, 
 | | |Desc_02671 <= 1.2, 
 | | | |Desc_03525 <= 1, 
 | | | | |Desc_02792 <= -0.58, class=1  (2/0) 
 | | | | |Desc_02792 >  -0.58, 
 | | | | | |Desc_01048 <= 1.11, class=-1  (44/5) 
 | | | | | |Desc_01048 >  1.11, class=1  (2/0) 
 | | | |Desc_03525 >  1, class=1  (3/0) 
 | | |Desc_02671 >  1.2, class=1  (4/0) 
 |Desc_02349 >  0.84, class=1  (12/0) 

 
  Total number of cases=76. 
  Number of misclassified cases=5.  Misclassification error = 6.57895%.
  Confusion matrix:
  Class -1   Class 1    <-- predicted 
  -------   -------   
       39         0    | Class -1
        5        32    | Class 1
  
  Kappa statistic: 0.867872. 
  Mutual Information: 0.70378. 
  Estimated misclassification rate on unseen set is 28.95% 
  (obtained by cross-validation).


_PREDICTION
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