A survey on online feature selection with streaming features. Hu, X., Zhou, P., Li, P., Wang, J., & Wu, X. Frontiers of Computer Science, 12(3):479–493, June, 2018. ![A survey on online feature selection with streaming features [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In the era of big data, the dimensionality of data is increasing dramatically in many domains. To deal with high dimensionality, online feature selection becomes critical in big data mining. Recently, online selection of dynamic features has received much attention. In situations where features arrive sequentially over time, we need to perform online feature selection upon feature arrivals. Meanwhile, considering grouped features, it is necessary to deal with features arriving by groups. To handle these challenges, some state-of-the-art methods for online feature selection have been proposed. In this paper, we first give a brief review of traditional feature selection approaches. Then we discuss specific problems of online feature selection with feature streams in detail. A comprehensive review of existing online feature selection methods is presented by comparing with each other. Finally, we discuss several open issues in online feature selection.
@article{hu_survey_2018,
title = {A survey on online feature selection with streaming features},
volume = {12},
issn = {2095-2236},
url = {https://doi.org/10.1007/s11704-016-5489-3},
doi = {10.1007/s11704-016-5489-3},
abstract = {In the era of big data, the dimensionality of data is increasing dramatically in many domains. To deal with high dimensionality, online feature selection becomes critical in big data mining. Recently, online selection of dynamic features has received much attention. In situations where features arrive sequentially over time, we need to perform online feature selection upon feature arrivals. Meanwhile, considering grouped features, it is necessary to deal with features arriving by groups. To handle these challenges, some state-of-the-art methods for online feature selection have been proposed. In this paper, we first give a brief review of traditional feature selection approaches. Then we discuss specific problems of online feature selection with feature streams in detail. A comprehensive review of existing online feature selection methods is presented by comparing with each other. Finally, we discuss several open issues in online feature selection.},
language = {en},
number = {3},
urldate = {2022-07-29},
journal = {Frontiers of Computer Science},
author = {Hu, Xuegang and Zhou, Peng and Li, Peipei and Wang, Jing and Wu, Xindong},
month = jun,
year = {2018},
keywords = {big data, feature selection, feature stream, online feature selection},
pages = {479--493},
}
Downloads: 0
{"_id":"zBve7JpM2oiNKiDZa","bibbaseid":"hu-zhou-li-wang-wu-asurveyononlinefeatureselectionwithstreamingfeatures-2018","author_short":["Hu, X.","Zhou, P.","Li, P.","Wang, J.","Wu, X."],"bibdata":{"bibtype":"article","type":"article","title":"A survey on online feature selection with streaming features","volume":"12","issn":"2095-2236","url":"https://doi.org/10.1007/s11704-016-5489-3","doi":"10.1007/s11704-016-5489-3","abstract":"In the era of big data, the dimensionality of data is increasing dramatically in many domains. To deal with high dimensionality, online feature selection becomes critical in big data mining. Recently, online selection of dynamic features has received much attention. In situations where features arrive sequentially over time, we need to perform online feature selection upon feature arrivals. Meanwhile, considering grouped features, it is necessary to deal with features arriving by groups. To handle these challenges, some state-of-the-art methods for online feature selection have been proposed. In this paper, we first give a brief review of traditional feature selection approaches. Then we discuss specific problems of online feature selection with feature streams in detail. A comprehensive review of existing online feature selection methods is presented by comparing with each other. Finally, we discuss several open issues in online feature selection.","language":"en","number":"3","urldate":"2022-07-29","journal":"Frontiers of Computer Science","author":[{"propositions":[],"lastnames":["Hu"],"firstnames":["Xuegang"],"suffixes":[]},{"propositions":[],"lastnames":["Zhou"],"firstnames":["Peng"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Peipei"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Jing"],"suffixes":[]},{"propositions":[],"lastnames":["Wu"],"firstnames":["Xindong"],"suffixes":[]}],"month":"June","year":"2018","keywords":"big data, feature selection, feature stream, online feature selection","pages":"479–493","bibtex":"@article{hu_survey_2018,\n\ttitle = {A survey on online feature selection with streaming features},\n\tvolume = {12},\n\tissn = {2095-2236},\n\turl = {https://doi.org/10.1007/s11704-016-5489-3},\n\tdoi = {10.1007/s11704-016-5489-3},\n\tabstract = {In the era of big data, the dimensionality of data is increasing dramatically in many domains. To deal with high dimensionality, online feature selection becomes critical in big data mining. Recently, online selection of dynamic features has received much attention. In situations where features arrive sequentially over time, we need to perform online feature selection upon feature arrivals. Meanwhile, considering grouped features, it is necessary to deal with features arriving by groups. To handle these challenges, some state-of-the-art methods for online feature selection have been proposed. In this paper, we first give a brief review of traditional feature selection approaches. Then we discuss specific problems of online feature selection with feature streams in detail. A comprehensive review of existing online feature selection methods is presented by comparing with each other. Finally, we discuss several open issues in online feature selection.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-07-29},\n\tjournal = {Frontiers of Computer Science},\n\tauthor = {Hu, Xuegang and Zhou, Peng and Li, Peipei and Wang, Jing and Wu, Xindong},\n\tmonth = jun,\n\tyear = {2018},\n\tkeywords = {big data, feature selection, feature stream, online feature selection},\n\tpages = {479--493},\n}\n\n\n\n","author_short":["Hu, X.","Zhou, P.","Li, P.","Wang, J.","Wu, X."],"key":"hu_survey_2018","id":"hu_survey_2018","bibbaseid":"hu-zhou-li-wang-wu-asurveyononlinefeatureselectionwithstreamingfeatures-2018","role":"author","urls":{"Paper":"https://doi.org/10.1007/s11704-016-5489-3"},"keyword":["big data","feature selection","feature stream","online feature selection"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["big data","feature selection","feature stream","online feature selection"],"search_terms":["survey","online","feature","selection","streaming","features","hu","zhou","li","wang","wu"],"title":"A survey on online feature selection with streaming features","year":2018}