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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...
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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