_PROBLEM CoEPrA-2006_Classification_001

_GROUP_NAME David Farrelly

_GROUP_MEMBERS
David Farrelly
Ernestine Lee
Kevin Hestir
Loren Hansen

_ADDRESS
Dr. David Farrelly
Department of Chemistry and Biochemistry
Utah State University
Logan, UT 84322-0300 USA

_MODELING_PROCEDURE
Name of method GENTREE

	First we randomly separated the training set
 of data into two groups of size 60 and size 29. 
The size 60 group was then used as a new training and
 the size 29 was set aside as the new test.
 This was done as a means to grade how well the
 method does at selecting descriptors. Descriptors 
would be selected based on the 60 cases and tested on
 the 29 cases. By doing so we could get a feel for 
how well we are doing. Then for the final run all 89
 cases would be combined and a new set of descriptors 
would be selected using the combined 89 these would
 be used to make the final predictions that would be turned in. 


	The classification method consists of two
 parts a heuristic optimization algorithm and a random forest 
of decision trees. First the given training set of data
 is randomly split to produce a smaller training 
set and a trial set. So in this case we would split the
 initial 60 to produce a smaller training set of size 40
 and a trial set of size 20. Then the heuristic optimization
 algorithm tries to find the subset of descriptors 
that best classifies the data in the trial set. Based on
 how well the random forest of decision trees does 
using the given subset of descriptors. The output of the
 optimization algorithm is a binary string of size 5787 
a one means use the given descriptor a zero means do not
 use. Then the two smaller datasets are recombined and
a new random selection takes place to produce a new
 training set of size 40 and trial set of size 20. And 
the optimization algorithm again tries to find the subset
 of descriptors that best classifies the new trial set. 
This process is repeated for many different random splits
 of the data into training and trial sets. 
Finally a count is made of how often each descriptor was
 used as part of the subset of descriptors obtained 
by the optimization algorithm. We would then look to see
 which descriptors are selected most often regardless 
of how the data was split and these become our final set.
 For example it may be 100 random splits where done
 and descriptor x was used in 75 of those splits as one of
 the subset of descriptors that best classifies 
the data and it could be descriptor y was used only 10 times.
 We would then conclude descriptor x is important 
and descriptor y is probably not. The final step was then to
 take our subset of descriptors used most often and 
see how well they do on an out of sample test set of data.
 By using this approach it was hoped that over fitting 
would be minimized. 
The optimization algorithm used in this case is a genetic
 algorithm.  We measured how well the given subset 
of descriptors does in classifying using a variety of metrics
 including area under a ROC curve, 
sensitivity and specificity and the error rate.  

_PREDICTION
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