2012 ©
Journal Publication
Title of Article Hybrid balancing technique using GRSOM and bootstap algorithms for classifiers with imbalanced data 
Date of Acceptance 1 May 2014 
     Title of Journal Advanced Materials Research 
     Standard SCOPUS 
     Institute of Journal Scientific.net 
     ISBN/ISSN 1662-8985 
     Volume 2014 
     Issue 931-932 
     Month May
     Year of Publication 2014 
     Page 1375-1381 
     Abstract To deal with imbalanced data, this paper proposes a hybrid data balancing technique which incorporates both over and under-sampling approaches. This technique determines how much minority data should be grown as well as how much majority data should be reduced. In this manner, noise introduced to the data due to excessive over-sampling could be avoided. On top of that, the proposed data balancing technique helps to determine the appropriate size of the balanced data and thus computation time required for construction of classifiers would be more efficient. The data balancing technique over samples the minority data through GRSOM method and then under samples the majority data using the bootstrap sampling approach. GRSOM is used in this study because it grows new samples in a non-linear fashion and preserves the original data structure. Performance of the proposed method is tested using four data sets from UCI Machine Learning Repository. Once the data sets are balanced, the committee of classifiers is constructed using these balanced data. The experimental results reveal that our proposed data balancing method provides the best performance. 
     Keyword Imbalanced data, data balancing technique, committee networks 
517040020-5 Miss SIRORAT PATTANAPAIROJ [Main Author]
Engineering Doctoral Degree

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