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

Baseline comparison of the weighted AUC (equation 4) to the 4,248 ProbeSets are made in
Figure 5.1. The feature subsets of lengths 43, 18, and 8 were selected for possessing gain criteria
greater than: 0.7, 0.8, and 0.9, respectively. The classification performance for these candidate
ProbeSet was assessed for the kNN and LDA classification algorithms [5-8]. The ProbeSets
selected by their gain criterion demonstrate classification performance improvement against the
whole model’s classification ability, and more significantly so for the kNN algorithm. While the
most ProbeSets identified by the most stringent threshold (greater than 0.9 information gain)
demonstrate a distinct loss of classification ability. In general, the linear discriminant analysis
demonstrates a marked improvement over the kNN classification algorithm, and not as significant
an improvement by down selection.
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