Method and computer program product for determining an efficient feature set and an optimal threshold confidence value for a pattern recogniton classifier
First Claim
1. A method of determining an efficient set of features and an optimal threshold confidence value for a pattern recognition system with at least one output class, comprising:
- selecting an initial set of features based upon an optimization algorithm;
classifying a plurality of pattern samples using the selected feature set;
optimizing a threshold confidence value to maximize the accuracy of the classification;
accepting the selected feature set and threshold confidence value if a cost function based upon classification accuracy meets a predetermined threshold cost function value; and
changing the feature set, by adding, removing or replacing a feature within the set based upon the optimization algorithm, if the cost function does not meet the predetermined threshold cost function value.
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Accused Products
Abstract
A method and computer program product are disclosed for determining an efficient set of features and an optimal confidence threshold value for a pattern recognition system with at least one output class. An initial set of features is selected based upon an optimization algorithm. A plurality of pattern samples are then classified using the selected feature set. A threshold confidence value is optimized as to maximize the accuracy of the classification. The selected feature set and threshold confidence value are accepted if a cost function based upon classification accuracy meets a predetermined threshold cost function value. The feature set is changed, by adding, removing or replacing a feature within the set based upon the optimization algorithm, if the cost function does not meet the predetermined threshold cost function value.
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Citations
20 Claims
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1. A method of determining an efficient set of features and an optimal threshold confidence value for a pattern recognition system with at least one output class, comprising:
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selecting an initial set of features based upon an optimization algorithm;
classifying a plurality of pattern samples using the selected feature set;
optimizing a threshold confidence value to maximize the accuracy of the classification;
accepting the selected feature set and threshold confidence value if a cost function based upon classification accuracy meets a predetermined threshold cost function value; and
changing the feature set, by adding, removing or replacing a feature within the set based upon the optimization algorithm, if the cost function does not meet the predetermined threshold cost function value. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer program product for determining an efficient set of features and an optimal threshold confidence value for a pattern recognition system with at least one output class, comprising:
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a selection portion that selects an initial set of features based upon an optimization algorithm;
a classification portion that classifies a plurality of pattern samples using the selected feature set;
a threshold optimization portion that optimizes a threshold confidence value to maximize the accuracy of the classification; and
an evaluation portion that accepts the selected feature set and threshold confidence value if a cost function based upon classification accuracy meets a predetermined cost function threshold and changes the feature set, by adding, removing or replacing a feature within the set based upon the optimization algorithm, if the cost function does not meet the predetermined cost function threshold. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification