IIR
2010
14 years 6 days ago
2010
We present four new feature selection methods for ordinal regression and test them against four different baselines on two large datasets of product reviews.
IJCSS
2000
14 years 2 months ago
2000
This paper uses
TKDE
2008
14 years 2 months ago
2008
Abstract-- Feature selection is an important method for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the ...
TEC
2008
14 years 2 months ago
2008
In general, the analysis of microarray data requires two steps: feature selection and classification. From a variety of feature selection methods and classifiers, it is difficult t...
JMLR
2006
14 years 2 months ago
2006
In streamwise feature selection, new features are sequentially considered for addition to a predictive model. When the space of potential features is large, streamwise feature sel...
ICPR
2010
IEEE
14 years 2 months ago
2010
IEEE
Abstract—We point out a problem inherent in the optimization scheme of many popular feature selection methods. It follows from the implicit assumption that higher feature selecti...
ICML
2010
IEEE
14 years 3 months ago
2010
IEEE
Many learning applications are characterized by high dimensions. Usually not all of these dimensions are relevant and some are redundant. There are two main approaches to reduce d...
ICML
2010
IEEE
14 years 3 months ago
2010
IEEE
We study an interesting and challenging problem, online streaming feature selection, in which the size of the feature set is unknown, and not all features are available for learni...
ADCS
2004
14 years 3 months ago
2004
In this paper we study the effectiveness of using a phrase-based representation in e-mail classification, and the affect this approach has on a number of machine learning algorithm...
CIKM
2008
Springer
14 years 4 months ago
2008
Springer
With the development of highly efficient graph data collection technology in many application fields, classification of graph data emerges as an important topic in the data mining...