Sample A: Cover Page of Thesis, Project, or Dissertation Proposal

averaged to give a final approximation of the gain ratio; where the randomness of initial selection
prevented the discovery of both solutions the single solution gain ratio was used. The gain ratio
was calculated as such:
gainratio ( X ) "
gain(
X )
, where (5.2)
splitin(
X )
n Ti
0 Ti
-
splitin(
X ) " ) ! 1 log2
.
+
(5.3)
i"
1 T / T ,
Here Ti represents the number of classification calls in each cluster [4]. As individual ProbeSet’s
inherent clustering ability increases, the information gain ratio approach 1.0 [3, 4, 10]. Down-
selection to the most informative ProbeSet features was set by a gain ratio threshold criterion.
For this dataset gain ratio limits of 0.7, 0.8, 0.9 yield ProbeSet lists of length 43, 18 and 8,
respectively. Average probe gains greater than or equal to 0.5, 0.6, and 0.7 yield ProbeSets lists
of length 95, 29 and 9.
Performance of the gain criterion down selected ProbeSets was monitored with supervised kNN
and LDA classification performances. Training and testing was done under 100 X 2 validations
with random splitting of samples in training and test sets, with sampling done with replacement
[3, 11, 12]. Sampling with replacement in such a scenario allows for the stochastic elimination
and pseudo-replication of samples, although replication may have not been consistent to the same
training/test set. Majority voting was implemented for the kNN algorithm, with the analysis of 3
nearest neighbors. The area under the receiver operating curve (AUC) was weighted for each of
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