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| Research on Dataset Feature Structure Based on Interaction Information |
| LIU Juan, ZHU Xiang-Ou, LIU Wen-Bing |
| College of Physics & Electronic Information Engineering, Wenzhou University, Wenzhou 325035 |
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Abstract In machine learning area, classification algorithms are widely studied and a large number of different types of algorithms are proposed. How to select appropriate ones from so many classification algorithms for the datasets becomes a crucial problem. Recently, a new method in reference [8] is proposed to characterize datasets and achieve better results in algorithm recommendation. In this paper, two methods are presented to characterize datasets under the theory of interaction information. The performance of 12 different types of classification algorithms on the 98 UCI datasets illustrates that both two-variable and three-variable interaction information methods can improve the precision and the hit rate of recommended algorithms. Furthermore, the latter performs even better under datasets with poor adaptability.
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Received: 22 November 2012
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2014, Vol. 27 
