Feature Selection, Association Rules Network and Theory Building

Sanjay Chawla
; Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, PMLR 10:14-21, 2010.

Abstract

As the size and dimensionality of data sets increase, the task of feature selection has become increasingly important. In this paper we demonstrate how association rules can be used to build a network of features, which we refer to as an association rules network, to extract features from large data sets. Association rules network can play a fundamental role in *theory building* - which is a task common to all data sciences- statistics, machine learning and data mining.

Cite this Paper


BibTeX
@InProceedings{pmlr-v10-chawla10a, title = {Feature Selection, Association Rules Network and Theory Building}, author = {Sanjay Chawla}, pages = {14--21}, year = {2010}, editor = {Huan Liu and Hiroshi Motoda and Rudy Setiono and Zheng Zhao}, volume = {10}, series = {Proceedings of Machine Learning Research}, address = {Hyderabad, India}, month = {21 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v10/chawla10a/chawla10a.pdf}, url = {http://proceedings.mlr.press/v10/chawla10a.html}, abstract = {As the size and dimensionality of data sets increase, the task of feature selection has become increasingly important. In this paper we demonstrate how association rules can be used to build a network of features, which we refer to as an association rules network, to extract features from large data sets. Association rules network can play a fundamental role in *theory building* - which is a task common to all data sciences- statistics, machine learning and data mining.} }
Endnote
%0 Conference Paper %T Feature Selection, Association Rules Network and Theory Building %A Sanjay Chawla %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-chawla10a %I PMLR %J Proceedings of Machine Learning Research %P 14--21 %U http://proceedings.mlr.press %V 10 %W PMLR %X As the size and dimensionality of data sets increase, the task of feature selection has become increasingly important. In this paper we demonstrate how association rules can be used to build a network of features, which we refer to as an association rules network, to extract features from large data sets. Association rules network can play a fundamental role in *theory building* - which is a task common to all data sciences- statistics, machine learning and data mining.
RIS
TY - CPAPER TI - Feature Selection, Association Rules Network and Theory Building AU - Sanjay Chawla BT - Proceedings of the Fourth International Workshop on Feature Selection in Data Mining PY - 2010/05/26 DA - 2010/05/26 ED - Huan Liu ED - Hiroshi Motoda ED - Rudy Setiono ED - Zheng Zhao ID - pmlr-v10-chawla10a PB - PMLR SP - 14 DP - PMLR EP - 21 L1 - http://proceedings.mlr.press/v10/chawla10a/chawla10a.pdf UR - http://proceedings.mlr.press/v10/chawla10a.html AB - As the size and dimensionality of data sets increase, the task of feature selection has become increasingly important. In this paper we demonstrate how association rules can be used to build a network of features, which we refer to as an association rules network, to extract features from large data sets. Association rules network can play a fundamental role in *theory building* - which is a task common to all data sciences- statistics, machine learning and data mining. ER -
APA
Chawla, S.. (2010). Feature Selection, Association Rules Network and Theory Building. Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, in PMLR 10:14-21

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