Reducing Cognitive Overload by Meta-Learning Assisted Algorithm Selection

Reducing Cognitive Overload by Meta-Learning Assisted Algorithm Selection

Lisa Fan (University of Regina, Canada) and Minxiao Lei (University of Regina, Canada)
DOI: 10.4018/978-1-60566-902-1.ch003
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Abstract

With the explosion of available data mining algorithms, a method for helping user to select the most appropriate algorithm or combination of algorithms to solve a given problem and reducing users’ cognitive overload due to the overloaded data mining algorithms is becoming increasingly important. This chapter presents a meta-learning approach to support users automatically selecting most suitable algorithms during data mining model building process. The authors discuss the meta-learning method in detail and present some empirical results that show the improvement that can be achieved with the hybrid model by combining meta-learning method and Rough Set feature reduction. The redundant properties of the dataset can be found. Thus, the ranking process can be sped up and accuracy can be increased by using the reduct of the properties of the dataset. With the reduced searching space, users’ cognitive load is reduced.
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Existing Meta-Learning Methods And Algorithm Selection Systems

Data Characterization Methods

Many methods have been developed to solve the problem of how to define a set of descriptors that can be used to describe a dataset in a way that can be used by the meta-learning method. The description methods can be considered data characterization. There are three main methods to solve the problem of how to define a set of descriptors. There are information/statistical properties-based data characterization, landmarking, and decision tree-based data characterization.

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