出版社：Faculty of Humanities, Kaunas University of Technology
摘要：Feature selection methods aim to improve the classification performance by eliminating non-valuable features. In this paper, our aim is to apply a recent optimization technique namely the Intelligent Water Drops (IWD) algorithm to select best features for sentiment analysis. We investigate the classification performances of our proposed IWD based feature selection method by comparing one of the well-known feature selection method using Maximum Entropy classifier. Experimental results show that Intelligent Water Drops based feature selection method outperforms than ReliefF method for sentiment analysis.
其他摘要：Based on the characteristics of mine goafs, the geo-electrical models are established. In order to study the response characteristics of goaf by transient electromagnetic method, we have performed three-dimensional numerical simulations in three cases, geo-electrical model of mine geology without goaf, geo-electrical model of mine geology with low resistivity goaf and geo-electrical model of mine geology with high resistivity goaf. Moreover, the physical experiments have also been performed for the above three cases. Furthermore, we compare numeral simulation results and physical experiment results. Finally, we obtain the conclusion that although the response of high resistivity goafs is weaker than that of low resistivity, its response characters still can indicate the existing of goaf. Hence, above work actually signifies that shallow high resistivity goafs can be detected in a reliable way by transient electromagnetic method. DOI: 10.5755/j01.eie.25.1.22733
