Methods and apparatus to integrate systematic data scaling into genetic algorithm-based feature subset selection
First Claim
1. A method of improving classification accuracy and reducing false positives in data mining, computer aided-detection, computer-aided diagnosis and artificial intelligence, the method comprising:
- choosing a training set from a set of training cases using systematic data scaling, the training set including one or more training cases for true nodules and one or more training cases for false nodules, the systematic data scaling removing only one or more training cases for false nodules, which is proximate a classification boundary for true and false nodules, from the training set; and
,creating a classifier based on the training set using a classification method, wherein the systematic data scaling method and the classification method produce the classifier thereby reducing false positives and improving classification accuracy.
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Abstract
Methods and apparatus for training a system for developing a process of data mining, false positive reduction, computer-aided detection, computer-aided diagnosis and artificial intelligence are provided. A method includes choosing a training set from a set of training cases using systematic data scaling and creating a classifier based on the training set using a classification method. The classifier yields fewer false positives. The method is suitable for use with a variety of data mining techniques including support vector machines, neural networks and decision trees.
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Citations
17 Claims
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1. A method of improving classification accuracy and reducing false positives in data mining, computer aided-detection, computer-aided diagnosis and artificial intelligence, the method comprising:
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choosing a training set from a set of training cases using systematic data scaling, the training set including one or more training cases for true nodules and one or more training cases for false nodules, the systematic data scaling removing only one or more training cases for false nodules, which is proximate a classification boundary for true and false nodules, from the training set; and
,creating a classifier based on the training set using a classification method, wherein the systematic data scaling method and the classification method produce the classifier thereby reducing false positives and improving classification accuracy. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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Specification