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

Figure 5.1: NSCLC ProbeSet gain selection model performances. Down selection of ProbeSets by the
ProbeSet gain criterion, as presented by the weighted AUC metric. Gain criterion was calculated for the kmeans
clustering performance of the average ProbeSet intensity. Left graphic is kNN implemented with
k=3 and majority voting and right graphic is LDA.
Down selection by the gain criterion calculated as the average probe clustering performance
across the ProbeSet yields similar classification performance for the LDA algorithm, and marked
improvement in the kNN classification algorithm. Results of the down selection through the
average probe gain criterion are presented in Figure 5.2. However, this is questionable approach
since the ProbeSet aggregate may possess minimal gain relevance. It is our belief that the
ProbeSets which are identified by both the ProbeSet and average probe gain criteria act as strong
classifiers, while the less informative ProbeSets which were identified by their average probe
information gain act as weak to noise classifiers.
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