Figure 8.

Optimal Feature Set Selection Algorithm. The function CLUSTER uses the k-means clustering approach to partition the initial gene set into the desired number of partitions K, with G genes in each partition. K and G are user-specified. The function SELECT_GENES uses either the greedy approach (Figure 7) or the heuristic simulated annealing approach to solve Problem 2. The function CLASSIFICATION_ERROR uses kNN classification method to assess the discriminant power of the selected genes and returns the classification error.