Reducing Cognitive Overload by Meta-Learning Assisted Algorithm Selection

With the explosion of available data mining algorithms, a method for helping user selecting the most appropriate algorithm or combination of algorithms to solve a problem and reducing cognitive overload due to the overloaded algorithms is becoming increasingly important. In this paper, we have explored a meta-learning approach to support user to automatically select most suited algorithms during data mining model building process. The paper discusses the meta-learning method in details and presents some preliminary 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 properties. With the reduced searching space, users cognitive load is reduced