@inproceedings{0e3a5455e67b4be4a23b7f91ce7757e0,
title = "FeatureMiner: A tool for interactive feature selection",
abstract = "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.",
keywords = "Data mining, Feature selection, Interactive user interface",
author = "Kewei Cheng and Jundong Li and Huan Liu",
note = "Funding Information: This material is, in part, supported by the National Science Foundation (NSF) under grant number IIS-1217466. Publisher Copyright: {\textcopyright} 2016 Copyright held by the owner/author(s).; 25th ACM International Conference on Information and Knowledge Management, CIKM 2016 ; Conference date: 24-10-2016 Through 28-10-2016",
year = "2016",
month = oct,
day = "24",
doi = "10.1145/2983323.2983329",
language = "English (US)",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "2445--2448",
booktitle = "CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management",
}