_PROBLEM CoEPrA-2006_Classification_004

_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 separated the training set of data
into two groups of size 61 and size 50. 
The size 61 group was then used as a new training
and the size 50 group 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 61 cases and
tested on the 50 cases. By doing so we could get a feel for 
how well we are doing. 

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 61 to produce a smaller training set of size 20
and a trial set of size 41. 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 20 and trial set of size 41. 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.  

Descriptors Used

A total of 28 Descriptors where used in the final model
Desc_01413	21
Desc_01605	28
Desc_01733	37
Desc_01875	26
Desc_02581	31
Desc_02582	33
Desc_02583	46
Desc_02604	65
Desc_02605	20
Desc_02681	57
Desc_02722	42
Desc_03021	20
Desc_03046	43
Desc_03047	50
Desc_03076	83
Desc_03077	43
Desc_03161	50
Desc_03360	20
Desc_04574	108
Desc_05154	27
Desc_05155	34
Desc_05281	23
Desc_05590	25
Desc_05591	22
Desc_05592	24
Desc_05596	26
Desc_05627	22
Desc_05734	21

_PREDICTION
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