This work discusses the theoretical abilities of three commonly used classifier learning methods and optimization techniques to cope with characteristics of real-world classification problems, more specifically varying misclassification costs, imbalanced data sets and varying degrees of hardness of class boundaries. From these discussions a universally applicable optimization framework is derived that successfully corrects the error-based inductive bias of classifier learning methods on image data within the domain of medical diagnosis. The framework was designed considering several points for improvement of common optimization techniques, such as the modification of the optimization procedure for inducer-specific parameters, the modification of input data by an arcing algorithm, and the combination of classifiers according to locally-adaptive, cost-sensitive voting schemes. The framework is designed to make the learning process cost-sensitive and to enforce more balanced misclassification costs between classes. Results on the evaluated domain are promising, while further improvements can be expected after some modifications to the framework.
This work discusses the theoretical abilities of three commonly used classifier learning methods and optimization techniques to cope with characteristics of real-world classification problems, more specifically varying misclassification costs, imbalanced data sets and varying degrees of hardness of class boundaries. From these discussions a universally applicable optimization framework is derived that successfully corrects the error-based inductive bias of classifier learning methods on image data within the domain of medical diagnosis. The framework was designed considering several points for improvement of common optimization techniques, such as the modification of the optimization procedure for inducer-specific parameters, the modification of input data by an arcing algorithm, and the combination of classifiers according to locally-adaptive, cost-sensitive voting schemes. The framework is designed to make the learning process cost-sensitive and to enforce more balanced misclassification costs between classes. Results on the evaluated domain are promising, while further improvements can be expected after some modifications to the framework.
This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the...
Learning Classifier Systems in Data Mining: An Introduction.- Data Mining in Proteomics with Learning Classifier Systems.- Improving Evolutionary Computation Based Data-Mining for the Process...
An overview of the theory and application of kernel classification methods.An overview of the theory and application of kernel classification methods.Linear classifiers in kernel spaces have emerged...
Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a...
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