Abstract
Chapter 6 (entitled “Computational Results on the Automatic Design of Full Rule Induction Algorithms”), reports the results of extensive experiments performed to evaluate the proposed genetic programming system. This chapter is divided into two parts. In the first part the experiments evaluate the system’s effectiveness in producing rule induction algorithms robust across different application domains. In this part of the chapter we report results involving a number of issues, namely: (a) we investigate the system’s sensitivity to values of some parameters (like crossover and mutation rates, and the number of datasets used to “train” the system); (b) we compare the predictive accuracies obtained by the automatically evolved rule induction algorithms with the accuracies obtained by baseline well-known human-designed rule induction algorithms; (c) we discuss to what extent the automatically evolved algorithms differ from the manually-designed ones; (d) we investigate the system’s sensitivity to variations in the grammar; (e) we compare the effectiveness of genetic programming and hill-climbing search as different methods for searching in the space of candidate rule induction algorithms; and (f) we evaluate a multiobjective version of the proposed genetic programming system, based on the Pareto optimality concept. In the second part of this chapter the experiments evaluate the system’s effectiveness in producing rule induction algorithms tailored to a particular application domain. We first report the results of experiments evolving rule induction algorithms tailored to individual datasets of the well-known UCI (University of California at Irvine) dataset repository, and then report the results of experiments in more challenging datasets in the area of bioinformatics, involving protein function prediction. A brief overview of the problem of protein function prediction is included in this part of the chapter, to make it more self-contained.
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Pappa, G.L., Freitas, A.A. (2010). Computational Results on the Automatic Design of Full Rule Induction Algorithms. In: Automating the Design of Data Mining Algorithms. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02541-9_6
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