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

Figure 5.3: NSCLC refined average probe gain selection model performances. Classification performance
of kNN (top) and Fisher’s linear discriminant analysis (bottom), as demonstrate by the weighted AUC per
individual disease class. Left column of graphs presents the performance of the full 4,248 ProbeSets and
right column of graphs present s the performance of the 13 ProbeSets with and average gain >= 0.6 with the
aggregate gain greater than average gain.
Discussion
Significant improvement in sample classification is demonstrated, with all three down selected
ProbeSets lists, for both classifiers. The linear discriminant analysis of the full model
demonstrates exceptionally good classification properties, much moreso than that of the kNN
algorithm. However the performance of the LDA algorithm is still improved through the
information gain down selection criterion. While the results from using most stringent gain
criterion, 0.9, indicate some over-training of the model, since both classifiers demonstrate
123