Feature Selection: An Ever Evolving Frontier in Data Mining

Huan Liu, Hiroshi Motoda, Rudy Setiono, Zheng Zhao
Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, PMLR 10:4-13, 2010.

Abstract

The rapid advance of computer technologies in data processing, collection, and storage has provided unparalleled opportunities to expand capabilities in production, services, communications, and research. However, immense quantities of high-dimensional data renew the challenges to the state-of-the-art data mining techniques. Feature selection is an effective technique for dimension reduction and an essential step in successful data mining applications. It is a research area of great practical significance and has been developed and evolved to answer the challenges due to data of increasingly high dimensionality. Its direct benefits include: building simpler and more comprehensible models, improving data mining performance, and helping prepare, clean, and understand data. We first briefly introduce the key components of feature selection, and review its developments with the growth of data mining. We then overview FSDM and the papers of FSDM10, which showcases of a vibrant research field of some contemporary interests, new applications, and ongoing research efforts. We then examine nascent demands in data-intensive applications and identify some potential lines of research that require multidisciplinary efforts.

Cite this Paper

BibTeX
@InProceedings{pmlr-v10-liu10b, title = {Feature Selection: An Ever Evolving Frontier in Data Mining}, author = {Liu, Huan and Motoda, Hiroshi and Setiono, Rudy and Zhao, Zheng}, booktitle = {Proceedings of the Fourth International Workshop on Feature Selection in Data Mining}, pages = {4--13}, year = {2010}, editor = {Liu, Huan and Motoda, Hiroshi and Setiono, Rudy and Zhao, Zheng}, volume = {10}, series = {Proceedings of Machine Learning Research}, address = {Hyderabad, India}, month = {21 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v10/liu10b/liu10b.pdf}, url = {https://proceedings.mlr.press/v10/liu10b.html}, abstract = {The rapid advance of computer technologies in data processing, collection, and storage has provided unparalleled opportunities to expand capabilities in production, services, communications, and research. However, immense quantities of high-dimensional data renew the challenges to the state-of-the-art data mining techniques. Feature selection is an effective technique for dimension reduction and an essential step in successful data mining applications. It is a research area of great practical significance and has been developed and evolved to answer the challenges due to data of increasingly high dimensionality. Its direct benefits include: building simpler and more comprehensible models, improving data mining performance, and helping prepare, clean, and understand data. We first briefly introduce the key components of feature selection, and review its developments with the growth of data mining. We then overview FSDM and the papers of FSDM10, which showcases of a vibrant research field of some contemporary interests, new applications, and ongoing research efforts. We then examine nascent demands in data-intensive applications and identify some potential lines of research that require multidisciplinary efforts.} }
Endnote
%0 Conference Paper %T Feature Selection: An Ever Evolving Frontier in Data Mining %A Huan Liu %A Hiroshi Motoda %A Rudy Setiono %A Zheng Zhao %B Proceedings of the Fourth International Workshop on Feature Selection in Data Mining %C Proceedings of Machine Learning Research %D 2010 %E Huan Liu %E Hiroshi Motoda %E Rudy Setiono %E Zheng Zhao %F pmlr-v10-liu10b %I PMLR %P 4--13 %U https://proceedings.mlr.press/v10/liu10b.html %V 10 %X The rapid advance of computer technologies in data processing, collection, and storage has provided unparalleled opportunities to expand capabilities in production, services, communications, and research. However, immense quantities of high-dimensional data renew the challenges to the state-of-the-art data mining techniques. Feature selection is an effective technique for dimension reduction and an essential step in successful data mining applications. It is a research area of great practical significance and has been developed and evolved to answer the challenges due to data of increasingly high dimensionality. Its direct benefits include: building simpler and more comprehensible models, improving data mining performance, and helping prepare, clean, and understand data. We first briefly introduce the key components of feature selection, and review its developments with the growth of data mining. We then overview FSDM and the papers of FSDM10, which showcases of a vibrant research field of some contemporary interests, new applications, and ongoing research efforts. We then examine nascent demands in data-intensive applications and identify some potential lines of research that require multidisciplinary efforts.
RIS
TY - CPAPER TI - Feature Selection: An Ever Evolving Frontier in Data Mining AU - Huan Liu AU - Hiroshi Motoda AU - Rudy Setiono AU - Zheng Zhao BT - Proceedings of the Fourth International Workshop on Feature Selection in Data Mining DA - 2010/05/26 ED - Huan Liu ED - Hiroshi Motoda ED - Rudy Setiono ED - Zheng Zhao ID - pmlr-v10-liu10b PB - PMLR DP - Proceedings of Machine Learning Research VL - 10 SP - 4 EP - 13 L1 - http://proceedings.mlr.press/v10/liu10b/liu10b.pdf UR - https://proceedings.mlr.press/v10/liu10b.html AB - The rapid advance of computer technologies in data processing, collection, and storage has provided unparalleled opportunities to expand capabilities in production, services, communications, and research. However, immense quantities of high-dimensional data renew the challenges to the state-of-the-art data mining techniques. Feature selection is an effective technique for dimension reduction and an essential step in successful data mining applications. It is a research area of great practical significance and has been developed and evolved to answer the challenges due to data of increasingly high dimensionality. Its direct benefits include: building simpler and more comprehensible models, improving data mining performance, and helping prepare, clean, and understand data. We first briefly introduce the key components of feature selection, and review its developments with the growth of data mining. We then overview FSDM and the papers of FSDM10, which showcases of a vibrant research field of some contemporary interests, new applications, and ongoing research efforts. We then examine nascent demands in data-intensive applications and identify some potential lines of research that require multidisciplinary efforts. ER -
APA
Liu, H., Motoda, H., Setiono, R. & Zhao, Z.. (2010). Feature Selection: An Ever Evolving Frontier in Data Mining. Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, in Proceedings of Machine Learning Research 10:4-13 Available from https://proceedings.mlr.press/v10/liu10b.html.

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