Text classification by weighted proximal support vector machine
First Claim
1. A system for training a text classifier, the system comprising:
- a text data preprocessor that preprocesses raw training text to produce an input matrix;
a weighting module that generates a weighted matrix by re-weighting the input matrix based on how many training examples are positive and how many training examples are negative; and
a model-vector generator that iteratively calculates a model vector based on the weighted matrix.
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Abstract
Embodiments of the invention relate to improvements to the support vector machine (SVM) classification model. When text data is significantly unbalanced (i.e., positive and negative labeled data are in disproportion), the classification quality of standard SVM deteriorates. Embodiments of the invention are directed to a weighted proximal SVM (WPSVM) model that achieves substantially the same accuracy as the traditional SVM model while requiring significantly less computational time. A weighted proximal SVM (WPSVM) model in accordance with embodiments of the invention may include a weight for each training error and a method for estimating the weights, which automatically solves the unbalanced data problem. And, instead of solving the optimization problem via the KKT (Karush-Kuhn-Tucker) conditions and the Sherman-Morrison-Woodbury formula, embodiments of the invention use an iterative algorithm to solve an unconstrained optimization problem, which makes WPSVM suitable for classifying relatively high dimensional data.
25 Citations
20 Claims
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1. A system for training a text classifier, the system comprising:
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a text data preprocessor that preprocesses raw training text to produce an input matrix;
a weighting module that generates a weighted matrix by re-weighting the input matrix based on how many training examples are positive and how many training examples are negative; and
a model-vector generator that iteratively calculates a model vector based on the weighted matrix. - View Dependent Claims (2, 3, 4)
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5. A system for classifying text, the system comprising:
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a text data preprocessor that preprocesses raw text to be classified to produce a vector representation of the text;
a model-vector reader that reads a model vector; and
a classifier that generates a classification result based on the vector representation of the text and based on the read model vector. - View Dependent Claims (6, 7, 8, 9)
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10. A computer-readable medium containing computer-executable instructions for training a text classifier and classifying text by performing steps comprising:
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representing input training text as a sparse matrix;
setting a plurality of classifier-training parameters;
iteratively solving a weighted proximal support vector machine equation; and
predicting respective classes for a plurality of test examples. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification