Feature selection in machine learning: A new perspective

Feature selection in machine learning: A new perspective. Cai, J., Luo, J., Wang, S., & Yang, S. Neurocomputing, 300:70–79, July, 2018.

Paper doi abstract bibtex

High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and facilitate a better understanding for the learning model or data. In this study, we discuss several frequently-used evaluation measures for feature selection, and then survey supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed.

@article{cai_feature_2018,
	title = {Feature selection in machine learning: {A} new perspective},
	volume = {300},
	issn = {0925-2312},
	shorttitle = {Feature selection in machine learning},
	url = {https://www.sciencedirect.com/science/article/pii/S0925231218302911},
	doi = {10.1016/j.neucom.2017.11.077},
	abstract = {High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and facilitate a better understanding for the learning model or data. In this study, we discuss several frequently-used evaluation measures for feature selection, and then survey supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed.},
	language = {en},
	urldate = {2021-08-19},
	journal = {Neurocomputing},
	author = {Cai, Jie and Luo, Jiawei and Wang, Shulin and Yang, Sheng},
	month = jul,
	year = {2018},
	keywords = {Data mining, Dimensionality reduction, Feature selection, Machine learning},
	pages = {70--79},
}

Downloads: 0

{"_id":"Ckw2R9iLjZqvA69E5","bibbaseid":"cai-luo-wang-yang-featureselectioninmachinelearninganewperspective-2018","author_short":["Cai, J.","Luo, J.","Wang, S.","Yang, S."],"bibdata":{"bibtype":"article","type":"article","title":"Feature selection in machine learning: A new perspective","volume":"300","issn":"0925-2312","shorttitle":"Feature selection in machine learning","url":"https://www.sciencedirect.com/science/article/pii/S0925231218302911","doi":"10.1016/j.neucom.2017.11.077","abstract":"High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and facilitate a better understanding for the learning model or data. In this study, we discuss several frequently-used evaluation measures for feature selection, and then survey supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed.","language":"en","urldate":"2021-08-19","journal":"Neurocomputing","author":[{"propositions":[],"lastnames":["Cai"],"firstnames":["Jie"],"suffixes":[]},{"propositions":[],"lastnames":["Luo"],"firstnames":["Jiawei"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Shulin"],"suffixes":[]},{"propositions":[],"lastnames":["Yang"],"firstnames":["Sheng"],"suffixes":[]}],"month":"July","year":"2018","keywords":"Data mining, Dimensionality reduction, Feature selection, Machine learning","pages":"70–79","bibtex":"@article{cai_feature_2018,\n\ttitle = {Feature selection in machine learning: {A} new perspective},\n\tvolume = {300},\n\tissn = {0925-2312},\n\tshorttitle = {Feature selection in machine learning},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0925231218302911},\n\tdoi = {10.1016/j.neucom.2017.11.077},\n\tabstract = {High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and facilitate a better understanding for the learning model or data. In this study, we discuss several frequently-used evaluation measures for feature selection, and then survey supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed.},\n\tlanguage = {en},\n\turldate = {2021-08-19},\n\tjournal = {Neurocomputing},\n\tauthor = {Cai, Jie and Luo, Jiawei and Wang, Shulin and Yang, Sheng},\n\tmonth = jul,\n\tyear = {2018},\n\tkeywords = {Data mining, Dimensionality reduction, Feature selection, Machine learning},\n\tpages = {70--79},\n}\n\n\n\n","author_short":["Cai, J.","Luo, J.","Wang, S.","Yang, S."],"key":"cai_feature_2018","id":"cai_feature_2018","bibbaseid":"cai-luo-wang-yang-featureselectioninmachinelearninganewperspective-2018","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S0925231218302911"},"keyword":["Data mining","Dimensionality reduction","Feature selection","Machine learning"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx","MF2nuJmkbpjPFYZ9g"],"keywords":["data mining","dimensionality reduction","feature selection","machine learning"],"search_terms":["feature","selection","machine","learning","new","perspective","cai","luo","wang","yang"],"title":"Feature selection in machine learning: A new perspective","year":2018}