Large classifier systems in bio- and cheminformatics

Large classifier systems are systems in machine learning that combine multiple classifiers. The goal is to improve the performance of classifiers for data sets of complex structure. This thesis introduces two algorithms that employ large classifier systems for multi-label and multi-relational classification. The usefulness of the approaches is shown on problems from bio- and cheminformatics, the prediction of biodegradation and the prediction of toxic effects.

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