Streaming feature selection algorithms for big data: A survey
Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Emerald Publishing
2022-03-01
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| Series: | Applied Computing and Informatics |
| Subjects: | |
| Online Access: | https://www.emerald.com/insight/content/doi/10.1016/j.aci.2019.01.001/full/pdf |
| _version_ | 1797796869282201600 |
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| author | Noura AlNuaimi Mohammad Mehedy Masud Mohamed Adel Serhani Nazar Zaki |
| author_facet | Noura AlNuaimi Mohammad Mehedy Masud Mohamed Adel Serhani Nazar Zaki |
| author_sort | Noura AlNuaimi |
| collection | DOAJ |
| description | Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research. |
| first_indexed | 2024-03-13T03:39:34Z |
| format | Article |
| id | doaj.art-89fd80cde14945f592942c94127e2fd8 |
| institution | Directory Open Access Journal |
| issn | 2634-1964 2210-8327 |
| language | English |
| last_indexed | 2024-03-13T03:39:34Z |
| publishDate | 2022-03-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | Applied Computing and Informatics |
| spelling | doaj.art-89fd80cde14945f592942c94127e2fd82023-06-23T09:38:00ZengEmerald PublishingApplied Computing and Informatics2634-19642210-83272022-03-01181/211313510.1016/j.aci.2019.01.001Streaming feature selection algorithms for big data: A surveyNoura AlNuaimi0Mohammad Mehedy Masud1Mohamed Adel Serhani2Nazar Zaki3College of Information Technology, United Arab Emirates University, Al Ain, United Arab EmiratesCollege of Information Technology, United Arab Emirates University, Al Ain, United Arab EmiratesCollege of Information Technology, United Arab Emirates University, Al Ain, United Arab EmiratesCollege of Information Technology, United Arab Emirates University, Al Ain, United Arab EmiratesOrganizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.https://www.emerald.com/insight/content/doi/10.1016/j.aci.2019.01.001/full/pdfBig dataRedundant featuresRelevant featuresStreaming feature groupingStreaming feature selection |
| spellingShingle | Noura AlNuaimi Mohammad Mehedy Masud Mohamed Adel Serhani Nazar Zaki Streaming feature selection algorithms for big data: A survey Applied Computing and Informatics Big data Redundant features Relevant features Streaming feature grouping Streaming feature selection |
| title | Streaming feature selection algorithms for big data: A survey |
| title_full | Streaming feature selection algorithms for big data: A survey |
| title_fullStr | Streaming feature selection algorithms for big data: A survey |
| title_full_unstemmed | Streaming feature selection algorithms for big data: A survey |
| title_short | Streaming feature selection algorithms for big data: A survey |
| title_sort | streaming feature selection algorithms for big data a survey |
| topic | Big data Redundant features Relevant features Streaming feature grouping Streaming feature selection |
| url | https://www.emerald.com/insight/content/doi/10.1016/j.aci.2019.01.001/full/pdf |
| work_keys_str_mv | AT nouraalnuaimi streamingfeatureselectionalgorithmsforbigdataasurvey AT mohammadmehedymasud streamingfeatureselectionalgorithmsforbigdataasurvey AT mohamedadelserhani streamingfeatureselectionalgorithmsforbigdataasurvey AT nazarzaki streamingfeatureselectionalgorithmsforbigdataasurvey |