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Abstract:

C4.5 is one of the tools for designing decision trees (DTs) from training examples. In most cases, C4.5 can generate near optimal DTs when the training data are given all together. However, if the training data are given incrementally, C4.5 cannot be used. In this case, genetic programming (GP) might be a better choice. Actually, GP can be considered as a DT-breeder in which good DTs can be generated automatically through evolution. In GP based DT design, the training examples can be given all together or incrementally, provided that the fitness of the tree is properly defined. This of course does NOT mean that the GP based approach is BETTER than C4.5 because DTs obtained by GP are usually very large and complex. In this paper, we try to integrate C4.5 and GP in such a way that each individual is initialized by C4.5 using part of the training examples. By so doing, we can have relatively good DTs from the very beginning, and use them while waiting for better DTs to emerge. To show the effectiveness of this kind of integration, we conducted some experiments with a digit recognition problem. Experimental results show that smaller DTs with higher recognition rates can always be obtained through integration of C4.5 and GP. However, as the evolution continues, DTs obtained by GP (with random initialization) tend to have almost the same recognition ability as those obtained by C4.5+GP. 1

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