Feature Selection: An Ever Evolving Frontier in Data Mining
@inproceedings{Liu2010FeatureSA,
title={Feature Selection: An Ever Evolving Frontier in Data Mining},
author={Huan Liu and Hiroshi Motoda and Rudy Setiono and Zheng Zhao},
booktitle={FSDM},
year={2010}
}- Published in FSDM 2010
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… CONTINUE READING
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References
Publications referenced by this paper.
SHOWING 1-10 OF 49 REFERENCES
An Integrative Approach to Indentifying Biologically Relevant Genes
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL
Ultrahigh Dimensional Feature Selection: Beyond The Linear Model
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL
Multi-Source Feature Selection via Geometry-Dependent Covariance Analysis
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL
Efficient Spectral Feature Selection with Minimum Redundancy
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL
Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL
Least angle regression
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL
1-norm Support Vector Machines
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL
Use of the zero norm with linear models and kernel methods
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL
Attribute Selection Based on FRiS-Compactness
VIEW 1 EXCERPT