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Radial basis function networks (RBFNs) have shown their capability to be used in classification problems, so that many data mining algorithms have been developed to configure RBFNs. These algorithms need to be given a suitable set of parameters for every problem they face, thus methods to automatically search the values of these parameters are required. This paper shows the robustness of a meta-algorithm...
For a given data set, different learning algorithms typically provide different classifiers. Although it is possible to simply select the most successful classifier, the less successful classifiers could have potentially valuable information that may be wasted. This work proposes GAESC, an algorithm for evolving a set of classifiers into a single symbolic classifier using genetic algorithms. Individuals...
Most symbolic classifiers aim at building sets of rules with good coverage and precision. While this is suitable for most applications, they tend to neglect other desirable properties, such as the ability to induce novel knowledge or to show new points of view of well-established concepts. An approach to overcome these limitations involves using a multi-objective evolutionary algorithm to build knowledge...
Real-world data are often prepared for purposes other than data mining and machine learning and, therefore, are represented by primitive attributes. When data representation is primitive, preprocessing data before looking for patterns becomes necessary. If lack of domain experts prevents the use of highly informative attributes, patterns are hard to uncover due to complex attribute interactions. This...
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