Constructive Induction using Genetic Programming (0) [7 citations — 1 self]
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author = {Hilan Bensusan and Ibrahim Kuscu},
title = {Constructive Induction using Genetic Programming},
booktitle = {In Evolutionary Computing and Machine Learning Workshop (ICML-96},
year = {},
publisher = {Morgan Kaufmann}
}
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Abstract:
A constructive induction model using genetic programming is presented. The model evolves new attributes starting from a random population of possible attributes constructed as functions of the original attributes. The model is tested on hard supervised learning problems and its performance is compared with backpropagation and C4.5. The performance of the system on learning incomplete 4-bit parity is reported to be better. 1 INTRODUCTION Constructive induction (CI) is an effort to improve the attribute vector of a learning problem in order to make the problem more easily learned for a particular learning algorithm(see [ 9 ] , [ 11 ] , [ 21 ] ). CI is often used to tackle hard problems for a given learning algorithm L - problems are hard for L if the training set contains all the relevant information for the induction of the target function but this information cannot be extracted by L (see [ 13 ] ). In general, CI is used to deal with problems that are hard for the most used learning ...
