Genetic Programming Symbolic Classification: A Study (original) (raw)
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Genetic programming (GP) is a powerful evolutionary algorithm introduced to evolve computer programs automatically. It is a domain independent, stochastic method with an important ability to represent programs of arbitrary size and shape. Its flexible nature has attracted numerous researchers in data mining community to use GP for classification. In this paper we have reviewed and analyzed tree based GP classification methods and propose taxonomy of these methods. We have also discussed various strengths and weaknesses of the technique and provide a framework to optimize the task of GP based classification.
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Identifying the mathematical relationships that best describe a dataset remains a very challenging problem in machine learning, and is known as Symbolic Regression (SR). In contrast to neural networks which are often treated as black boxes, SR attempts to gain insight into the underlying relationships between the independent variables and the target variable of a given dataset by assembling analytical functions. In this paper, we present GSR, a Generalized Symbolic Regression approach, by modifying the conventional SR optimization problem formulation, while keeping the main SR objective intact. In GSR, we infer mathematical relationships between the independent variables and some transformation of the target variable. We constrain our search space to a weighted sum of basis functions, and propose a genetic programming approach with a matrix-based encoding scheme. We show that our GSR method is competitive with strong SR benchmark methods, achieving promising experimental performance on the well-known SR benchmark problem sets. Finally, we highlight the strengths of GSR by introducing SymSet, a new SR benchmark set which is more challenging relative to the existing benchmarks.
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In this paper we present a novel way 0/ combining symbolic inductive methods and genetic algorithms (GAs) applied to produce high-performance classification rules. The presented method consists 0/ two phases. In the first one the algorithm induvtively learns a set of classification rules from noisy input examples. In the second phase the worst performing rule is optimized by GAs techniques. Experimental results are presented/or twelve classes o/noisy data obtained/rom textured images.
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Lecture Notes in Computer Science, 2005
A new genetic programming based approach to classification problems is proposed. Differently from other approaches, the number of prototypes in the classifier is not a priori fixed, but automatically found by the system. In fact, in many problems a single class may contain a variable number of subclasses. Hence, a single prototype, may be inadequate to represent all the members of the class. The devised approach has been tested on several problems and the results compared with those obtained by a different genetic programming based approach recently proposed in the literature.
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