TY - GEN
T1 - FeatureMiner
T2 - 25th ACM International Conference on Information and Knowledge Management, CIKM 2016
AU - Cheng, Kewei
AU - Li, Jundong
AU - Liu, Huan
N1 - Funding Information:
This material is, in part, supported by the National Science Foundation (NSF) under grant number IIS-1217466.
Publisher Copyright:
© 2016 Copyright held by the owner/author(s).
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2016/10/24
Y1 - 2016/10/24
N2 - The recent popularity of big data has brought immense quantities of high-dimensional data, which presents challenges to traditional data mining tasks due to curse of dimensionality. Feature selection has shown to be effective to prepare these high dimensional data for a variety of learning tasks. To provide easy access to feature selection algorithms, we provide an interactive feature selection tool FeatureMiner based on our recently released feature selection repository scikit-feature1. FeatureMiner eases the process of performing feature selection for practitioners by providing an interactive user interface. Meanwhile, it also gives users some practical guidance in finding a suitable feature selection algorithm among many given a specific dataset. In this demonstration, we show (1) How to conduct data preprocessing after loading a dataset; (2) How to apply feature selection algorithms; (3) How to choose a suitable algorithm by visualized performance evaluation.
AB - The recent popularity of big data has brought immense quantities of high-dimensional data, which presents challenges to traditional data mining tasks due to curse of dimensionality. Feature selection has shown to be effective to prepare these high dimensional data for a variety of learning tasks. To provide easy access to feature selection algorithms, we provide an interactive feature selection tool FeatureMiner based on our recently released feature selection repository scikit-feature1. FeatureMiner eases the process of performing feature selection for practitioners by providing an interactive user interface. Meanwhile, it also gives users some practical guidance in finding a suitable feature selection algorithm among many given a specific dataset. In this demonstration, we show (1) How to conduct data preprocessing after loading a dataset; (2) How to apply feature selection algorithms; (3) How to choose a suitable algorithm by visualized performance evaluation.
KW - Data mining
KW - Feature selection
KW - Interactive user interface
UR - http://www.scopus.com/inward/record.url?scp=84996480096&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84996480096&partnerID=8YFLogxK
U2 - 10.1145/2983323.2983329
DO - 10.1145/2983323.2983329
M3 - Conference contribution
AN - SCOPUS:84996480096
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2445
EP - 2448
BT - CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 24 October 2016 through 28 October 2016
ER -