TY - GEN
T1 - Featureselector
T2 - 2014 Annual Conference on Extreme Science and Engineering Discovery Environment, XSEDE 2014
AU - Cai, Y. Dora
AU - Riedl, Bettina Cassandra
AU - Ratan, Rabindra Robby
AU - Shen, Cuihua
AU - Picot, Arnold
N1 - The work carried out in the author's laboratory is supported by the National Multiple Sclerosis Society, the NIH, the United States-Israel Science Foundation (BSF), Jerusalem, Israel, the Minerva Foundation, and the Israel Science Foundation. E.P. is an Incumbent of the Madeleine Haas Russell Career Development Chair.
PY - 2014/7/1
Y1 - 2014/7/1
N2 - Due to the huge volume and extreme complexity in online game data collections, selecting essential features for the analysis of massive game logs is not only necessary, but also challenging. This study develops and implements a new XSEDE-enabled tool, FeatureSelector, which uses the parallel processing techniques on high performance computers to perform feature selection. By calculating probability distance measures, based on K-L divergence, this tool quantifies the distance between variables in data sets, and provides guidance for feature selection in massive game log analysis. This tool has helped researchers choose the high-quality and discriminative features from over 300 variables, and select the top pairs of countries with the greatest differences from 231 country-pairs in a 500 GB game log data set. Our study shows that (1) K-L divergence is a good measure for correctly and efficiently selecting important features, and (2) the high performance computing platform supported by XSEDE has substantially accelerated the feature selection processes by over 30 times. Besides demonstrating the effectiveness of FeatureSelector in a cross-country analysis using high performance computing, this study also highlights some lessons learned for feature selection in social science research and some experience on applying parallel processing techniques in intensive data analysis.
AB - Due to the huge volume and extreme complexity in online game data collections, selecting essential features for the analysis of massive game logs is not only necessary, but also challenging. This study develops and implements a new XSEDE-enabled tool, FeatureSelector, which uses the parallel processing techniques on high performance computers to perform feature selection. By calculating probability distance measures, based on K-L divergence, this tool quantifies the distance between variables in data sets, and provides guidance for feature selection in massive game log analysis. This tool has helped researchers choose the high-quality and discriminative features from over 300 variables, and select the top pairs of countries with the greatest differences from 231 country-pairs in a 500 GB game log data set. Our study shows that (1) K-L divergence is a good measure for correctly and efficiently selecting important features, and (2) the high performance computing platform supported by XSEDE has substantially accelerated the feature selection processes by over 30 times. Besides demonstrating the effectiveness of FeatureSelector in a cross-country analysis using high performance computing, this study also highlights some lessons learned for feature selection in social science research and some experience on applying parallel processing techniques in intensive data analysis.
KW - Feature selection
KW - Game log analysis
KW - K-L divergence
KW - Massively multiplayer online games
UR - https://www.scopus.com/pages/publications/84905513104
UR - https://www.scopus.com/pages/publications/84905513104#tab=citedBy
U2 - 10.1145/2616498.2616511
DO - 10.1145/2616498.2616511
M3 - Conference contribution
AN - SCOPUS:84905513104
SN - 9781450328937
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the XSEDE 2014 Conference
PB - Association for Computing Machinery
Y2 - 13 July 2014 through 18 July 2014
ER -