Method for feature selection in a support vector machine using feature ranking
First Claim
1. A computer-implemented method for predicting patterns in a dataset, wherein the data comprises a large set of features that describe the data, wherein each feature has a feature value corresponding to each datapoint within the dataset, the method comprising:
- identifying a subset of significant features that are most correlated to the patterns, comprising;
downloading a dataset having known outcomes into a memory of a computer having a processor for executing a classification algorithm;
for each feature, separating the data into classes according to their known outcomes, wherein the classes comprise a first class having a first set of feature values and a second class having second set of feature values;
for each feature, calculating an extremal difference in feature value between a lowest feature value in the first class and a highest feature value in the second class;
ranking the features according to the extremal differences in feature value between the classes, wherein the highest extremal differences in feature value have the highest ranks;
generating an output in the memory comprising the subset of features having the highest ranks, wherein the subset of features is correlated to the patterns; and
transferring the output from the memory to a media.
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Accused Products
Abstract
In a pre-processing step prior to training a learning machine, pre-processing includes reducing the quantity of features to be processed using feature selection methods selected from the group consisting of recursive feature elimination (RFE), minimizing the number of non-zero parameters of the system (l0-norm minimization), evaluation of cost function to identify a subset of features that are compatible with constraints imposed by the learning set, unbalanced correlation score, transductive feature selection and single feature using margin-based ranking. The features remaining after feature selection are then used to train a learning machine for purposes of pattern classification, regression, clustering and/or novelty detection.
77 Citations
27 Claims
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1. A computer-implemented method for predicting patterns in a dataset, wherein the data comprises a large set of features that describe the data, wherein each feature has a feature value corresponding to each datapoint within the dataset, the method comprising:
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identifying a subset of significant features that are most correlated to the patterns, comprising; downloading a dataset having known outcomes into a memory of a computer having a processor for executing a classification algorithm; for each feature, separating the data into classes according to their known outcomes, wherein the classes comprise a first class having a first set of feature values and a second class having second set of feature values; for each feature, calculating an extremal difference in feature value between a lowest feature value in the first class and a highest feature value in the second class; ranking the features according to the extremal differences in feature value between the classes, wherein the highest extremal differences in feature value have the highest ranks; generating an output in the memory comprising the subset of features having the highest ranks, wherein the subset of features is correlated to the patterns; and transferring the output from the memory to a media. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A computer-implemented method for predicting patterns in a dataset, wherein the data comprises a large set of features that describe the data, wherein each feature has a feature value corresponding to each datapoint within the dataset, the method comprising:
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identifying a subset of significant features that are most correlated to the patterns, comprising; downloading a dataset having known outcomes into a memory of a computer having a processor for executing a classification algorithm; using the classification algorithm, separating the dataset into two or more classes according to the known outcomes; for each feature, determining a separation between extremal feature value points within the two or more classes; and ranking the subset of features according to the size of the separation for each feature, wherein the feature with the largest separation is assigned the highest rank; generating an output in the memory comprising the subset of features having the highest ranks, wherein the subset of features is correlated to the patterns; and transferring the output from the memory to a media. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A computer program product embodied on a computer readable medium for predicting patterns in data by identifying a subset of significant features that are most correlated to the patterns, wherein the data comprises a large set of features that describe the data, the computer program product comprising instructions for executing a classification algorithm and further for causing a computer processor to:
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(a) receive the data; (b) using the classification algorithm, separating the dataset into two or more classes according to the known outcomes; (c) for each feature, determining a separation between extremal feature value points within the two or more classes of interest; and (d) ranking the subset of features according to the size of the separation for each feature, wherein the feature with the largest separation corresponds to is assigned the highest rank; and (e) generating an output in the memory comprising the subset of features having the highest ranks, wherein the subset of features is correlated to the patterns. - View Dependent Claims (26, 27)
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Specification