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SDM
2007
SIAM
137views Data Mining» more  SDM 2007»
9 years 1 months ago
Semi-supervised Feature Selection via Spectral Analysis
Feature selection is an important task in effective data mining. A new challenge to feature selection is the so-called “small labeled-sample problem” in which labeled data is...
Zheng Zhao, Huan Liu
SDM
2008
SIAM
136views Data Mining» more  SDM 2008»
9 years 1 months ago
Exploration and Reduction of the Feature Space by Hierarchical Clustering
In this paper we propose and test the use of hierarchical clustering for feature selection. The clustering method is Ward's with a distance measure based on GoodmanKruskal ta...
Dino Ienco, Rosa Meo
SDM
2008
SIAM
117views Data Mining» more  SDM 2008»
9 years 1 months ago
A Feature Selection Algorithm Capable of Handling Extremely Large Data Dimensionality
With the advent of high throughput technologies, feature selection has become increasingly important in a wide range of scientific disciplines. We propose a new feature selection ...
Yijun Sun, Sinisa Todorovic, Steve Goodison
AAAI
2010
9 years 1 months ago
Efficient Spectral Feature Selection with Minimum Redundancy
Spectral feature selection identifies relevant features by measuring their capability of preserving sample similarity. It provides a powerful framework for both supervised and uns...
Zheng Zhao, Lei Wang, Huan Liu
PAKDD
2000
ACM
124views Data Mining» more  PAKDD 2000»
9 years 3 months ago
Feature Selection for Clustering
In clustering, global feature selection algorithms attempt to select a common feature subset that is relevant to all clusters. Consequently, they are not able to identify individu...
Manoranjan Dash, Huan Liu
KDD
2010
ACM
310views Data Mining» more  KDD 2010»
9 years 3 months ago
An integrated machine learning approach to stroke prediction
Stroke is the third leading cause of death and the principal cause of serious long-term disability in the United States. Accurate prediction of stroke is highly valuable for early...
Aditya Khosla, Yu Cao, Cliff Chiung-Yu Lin, Hsu-Ku...
RSKT
2009
Springer
9 years 4 months ago
A Time-Reduction Strategy to Feature Selection in Rough Set Theory
In rough set theory, the problem of feature selection aims to retain the discriminatory power of original features. Many feature selection algorithms have been proposed, however, q...
Hongxing Chen, Yuhua Qian, Jiye Liang, Wei Wei, Fe...
HAIS
2009
Springer
9 years 4 months ago
Unsupervised Feature Selection in High Dimensional Spaces and Uncertainty
Developing models and methods to manage data vagueness is a current effervescent research field. Some work has been done with supervised problems but unsupervised problems and unce...
José Ramón Villar, María del ...
ICDM
2005
IEEE
139views Data Mining» more  ICDM 2005»
9 years 5 months ago
Stability of Feature Selection Algorithms
With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive ...
Alexandros Kalousis, Julien Prados, Melanie Hilari...
ICRA
2008
IEEE
170views Robotics» more  ICRA 2008»
9 years 6 months ago
Human detection using iterative feature selection and logistic principal component analysis
— We present a fast feature selection algorithm suitable for object detection applications where the image being tested must be scanned repeatedly to detected the object of inter...
Wael Abd-Almageed, Larry S. Davis
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