System and method of feature selection for text...

Details System and method of feature selection for text... System and method of feature selection for text...

2007-12-31

2011-10-25

Gaffin, Jeffrey A (Department: 2129)

Data processing: artificial intelligence

Knowledge processing system

C706S012000

Reexamination Certificate

active

08046317

ABSTRACT:
An improved system and method is provided for feature selection for text classification using subspace sampling. A text classifier generator may be provided for selecting a small set of features using subspace sampling from the corpus of training data to train a text classifier for using the small set of features for classification of texts. To select the small set of features, a subspace of features from the corpus of training data may be randomly sampled according to a probability distribution over the set of features where a probability may be assigned to each of the features that is proportional to the square of the Euclidean norms of the rows of left singular vectors of a matrix of the features representing the corpus of training texts. The small set of features may classify texts using only the relevant features among a very large number of training features.

REFERENCES:
patent: 2008/0082475 (2008-04-01), Aggarwal et al.
Dasgupta et al (“Feature Selection Methods for Text Classification” KDD'07).
Drineas et al (“Relative-Error CUR Matrix Decompositions” Aug. 27, 2007), downloaded at http://arxiv.org/PS—cache/arxiv/pdf/0708/0708.3696v1.pdf.
Drineas et al (“Sampling Algorithms and Coresets for Lp Regression” Jul. 11, 2007), downloaded at http://arxiv.org/PS—cache/arxiv/pdf/0707/0707.1714v1.pdf.
Drineas et al (“Sampling algorithms for I2 regression and applications” Jan. 2006), downloaded at http://portal.acm.org/citation.cfm?id=1109557.1109682.
Gabrilovich et al (“Text Categorization with Many Redundant Features: Using Aggressive Feature Selection to Make SVMs Competitive with C4.5” 2004).
Drineas et al (“Subspace Sampling and Relative-Error Matrix Approximation: Column-Based Methods” APPROX and RANDOM Aug. 28-30, 2006).

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