A novel approach to design classifiers using genetic programming
We propose a new approach for designing classifiers for a c-class (c greater than or equal to 2) problem using. genetic programming (GP). The proposed approach takes an integrated view of all classes when the GP evolves. A multitree representation of chromosomes is used. In this context, we propose a modified crossover operation and a new mutation operation that reduces the destructive nature of conventional genetic operations. We use a new concept of unfitness of a tree to select trees for genetic operations. This gives more opportunity to unfit trees to become fit. A new concept of OR-ing chromosomes in the terminal population is introduced, which enables us to get a classifier with better performance. Finally, a weight-based scheme and some heuristic rules characterizing typical ambiguous situations are used for conflict resolution. The classifier is capable of saying, "don't know" when faced with unfamiliar examples. The effectiveness of our scheme is demonstrated on several real data sets.
classifier genetic programming (gp) multitree representation multicategory pattern classification nondestructive directed point mutation or-ing operation