_PROBLEM CoEPrA-2006_Classification_004

_GROUP_NAME Scott Oloff

_GROUP_MEMBERS
Scott Oloff

_ADDRESS
Boehringer Ingelheim
900 Ridgebury Rd.
Ridgefield, CT 06877

_MODELING_PROCEDURE
I used a newly developed machine learning approach called kScore, which
is based on kNN and SVM technologies.  The approach is kernel free and
uses all training compounds in a distance-weighted manner to predict a
test compound rather than just its k Nearest Neighbors.  The algorithm
also incorporates Generalization constraints, a soft margin classifier
(for Classification), and an Epsilon Loss function (for Regression)
similar to those proposed by V. Vapnik.  There is a built in
Applicability Domain for each kScore model and a more detailed
explanation of the method is being submitted for publication.

The descriptors of the original training set were normalized from 0 to
1.  The training set was then randomly divided into 5 training and test
sets whereby the test set contained 20% of the compounds.  For each
training/test split several kScore models were generated (using the
CoEPrA provided descriptors) to find the best values of C and Epsilon
for external prediction.  The most predictive models were collected and
used in a consensus fashion to predict the external test set.  The
average LOO Training accuracies were 0.7-0.8 and the average Test
accuracies for each class varied between 0.6 and 0.8.  The most
predictive values of Epsilon and C varied from 0.2 to 0.6 and 200 to
500, respectively.  The list of weights for all models in this 
consensus
prediction is quite large however I will email them to anyone
interested.

The objects that fell just outside of the models' applicability domain
but were predicted anyway for the purpose of this competition are:
Obj_00002
Obj_00027
Obj_00056
Obj_00060
Obj_00062
Obj_00064
Obj_00076
Obj_00093
Obj_00102
Obj_00103


_PREDICTION
Obj_00001 -1
Obj_00002 -1
Obj_00003 -1
Obj_00004 -1
Obj_00005 -1
Obj_00006 -1
Obj_00007 -1
Obj_00008 -1
Obj_00009 -1
Obj_00010 -1
Obj_00011 -1
Obj_00012 -1
Obj_00013 -1
Obj_00014 +1
Obj_00015 -1
Obj_00016 +1
Obj_00017 -1
Obj_00018 -1
Obj_00019 -1
Obj_00020 -1
Obj_00021 -1
Obj_00022 +1
Obj_00023 -1
Obj_00024 -1
Obj_00025 -1
Obj_00026 -1
Obj_00027 -1
Obj_00028 -1
Obj_00029 -1
Obj_00030 -1
Obj_00031 -1
Obj_00032 -1
Obj_00033 +1
Obj_00034 -1
Obj_00035 +1
Obj_00036 -1
Obj_00037 -1
Obj_00038 -1
Obj_00039 -1
Obj_00040 -1
Obj_00041 -1
Obj_00042 -1
Obj_00043 -1
Obj_00044 -1
Obj_00045 -1
Obj_00046 -1
Obj_00047 -1
Obj_00048 -1
Obj_00049 -1
Obj_00050 -1
Obj_00051 -1
Obj_00052 -1
Obj_00053 -1
Obj_00054 -1
Obj_00055 +1
Obj_00056 +1
Obj_00057 -1
Obj_00058 -1
Obj_00059 -1
Obj_00060 -1
Obj_00061 -1
Obj_00062 -1
Obj_00063 -1
Obj_00064 +1
Obj_00065 -1
Obj_00066 -1
Obj_00067 -1
Obj_00068 -1
Obj_00069 -1
Obj_00070 -1
Obj_00071 +1
Obj_00072 -1
Obj_00073 -1
Obj_00074 -1
Obj_00075 -1
Obj_00076 -1
Obj_00077 -1
Obj_00078 -1
Obj_00079 -1
Obj_00080 -1
Obj_00081 -1
Obj_00082 -1
Obj_00083 -1
Obj_00084 -1
Obj_00085 -1
Obj_00086 -1
Obj_00087 -1
Obj_00088 -1
Obj_00089 -1
Obj_00090 -1
Obj_00091 -1
Obj_00092 -1
Obj_00093 +1
Obj_00094 -1
Obj_00095 -1
Obj_00096 -1
Obj_00097 -1
Obj_00098 -1
Obj_00099 +1
Obj_00100 -1
Obj_00101 -1
Obj_00102 +1
Obj_00103 +1
Obj_00104 -1
Obj_00105 -1
Obj_00106 -1
Obj_00107 -1
Obj_00108 -1
Obj_00109 -1
Obj_00110 +1
Obj_00111 -1