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/jcini.2008070107
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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. In this article, we have presented a meta-learning approach to support users automatically selecting most suitable algorithms during data mining model building process. The article discusses the meta-learning method in details and presents some empirical results that show the improvement we can achieve with the hybrid model by combining meta-learning method and Rough Set feature reduction. The redundant properties of the dataset can be found. Thus, we can speed up the ranking process and increase the accuracy by using the reduct of the properties of the dataset. With the reduced searching space, users’ cognitive load is reduced.

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