Granular support vector machine with random granularity
First Claim
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1. A method comprising:
- receiving a training dataset comprising a plurality of tuples and a plurality of attributes for each of the tuples;
deriving a plurality of granules from the training dataset, each granule comprising a plurality of sample tuples and a plurality of sample attributes, wherein for each of the plurality of granules;
the plurality of sample tuples is randomly selected from among the plurality of tuples with replacement; and
the plurality of sample attributes is randomly selected from among the plurality of attributes without replacement;
processing the granules using a support vector machine process to identify a hyperplane classifier associated with each of the granules;
predicting a classification of a new tuple using each of the hyperplane classifiers to produce a plurality of predictions;
aggregating the predictions to derive a decision on a final classification of the new tuple;
validating a first hyperplane classifier associated with a granule by classifying a plurality of tuples from the training dataset which were not included in the granule;
generating a hyperplane classifier effectiveness level based upon the validation of the first hyperplane classifier against tuples from the training dataset which were not included in the granule;
determining whether the hyperplane classifier effectiveness level exceeds a threshold effectiveness level; and
in response to determining that the hyperplane classifier effectiveness level does not exceed the threshold effectiveness level;
removing the first hyperplane classifier; and
requesting a second plurality of sample attributes, each sample attribute in the second plurality of sample attributes different from each sample attribute in the plurality of sample attributes, for use in identifying new hyperplane classifiers.
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Abstract
Methods and systems for granular support vector machines. Granular support vector machines can randomly select samples of datapoints and project the samples of datapoints into a randomly selected subspaces to derive granules. A support vector machine can then be used to identify hyperplane classifiers respectively associated with the granules. The hyperplane classifiers can be used on an unknown datapoint to provide a plurality of predictions which can be aggregated to provide a final prediction associated with the datapoint.
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Citations
23 Claims
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1. A method comprising:
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receiving a training dataset comprising a plurality of tuples and a plurality of attributes for each of the tuples; deriving a plurality of granules from the training dataset, each granule comprising a plurality of sample tuples and a plurality of sample attributes, wherein for each of the plurality of granules; the plurality of sample tuples is randomly selected from among the plurality of tuples with replacement; and the plurality of sample attributes is randomly selected from among the plurality of attributes without replacement; processing the granules using a support vector machine process to identify a hyperplane classifier associated with each of the granules; predicting a classification of a new tuple using each of the hyperplane classifiers to produce a plurality of predictions; aggregating the predictions to derive a decision on a final classification of the new tuple; validating a first hyperplane classifier associated with a granule by classifying a plurality of tuples from the training dataset which were not included in the granule; generating a hyperplane classifier effectiveness level based upon the validation of the first hyperplane classifier against tuples from the training dataset which were not included in the granule; determining whether the hyperplane classifier effectiveness level exceeds a threshold effectiveness level; and in response to determining that the hyperplane classifier effectiveness level does not exceed the threshold effectiveness level; removing the first hyperplane classifier; and requesting a second plurality of sample attributes, each sample attribute in the second plurality of sample attributes different from each sample attribute in the plurality of sample attributes, for use in identifying new hyperplane classifiers. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A system for performing the disclosed methods) comprising:
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one or more computers; one or more memory devices in data communication with the one or more computers and storing instructions defining; a granule selection module operable to select a plurality of granules from a training dataset, each of the granules comprising a plurality of tuples and a plurality of attributes, wherein, for each of the plurality of granules, the plurality of tuples are randomly selected with replacement and the plurality of attributes are randomly selected without replacement; a plurality of granule processing modules operable to process granules using a support vector machine process to identify a hyperplane classifier associated with each of the granules; one or more prediction modules operable to predict a classification associated with an unknown tuple based upon the hyperplane classifiers to produce a plurality of granule predictions; and an aggregation module operable to aggregate the granule predictions to derive a decision on a final classification associated with the unknown tuple; and a validation module operable to; validate a first hyperplane classifier associated with a granule by attempting to classify a plurality of tuples from the training dataset which were not included in the granule; generate a hyperplane classifier effectiveness level based upon the validation of the first hyperplane classifier against tuples from the training dataset which were not included in the granule; determine whether the hyperplane classifier effectiveness level exceeds a threshold effectiveness level; and in response to determining that the hyperplane classifier effectiveness level does not exceed the threshold effectiveness level; remove the first hyperplane classifier; and request a second plurality of attributes, each attribute in the second plurality of sample attributes different from each attribute in the plurality of attributes, for use in identifying new hyperplane classifiers. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21)
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22. A method comprising:
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receiving a training dataset comprising a plurality of tuples and a plurality of attributes for each of the tuples; deriving a plurality of granules from the training dataset, each granule comprising a plurality of sample tuples and a plurality of sample attributes, wherein for each of the plurality of granules; the plurality of sample tuples is randomly selected from among the plurality of tuples with replacement; and the plurality of sample attributes is randomly selected from among the plurality of attributes without replacement; processing the granules using a support vector machine process to identify a hyperplane classifier associated with each of the granules; predicting a classification of a new tuple using each of the hyperplane classifiers to produce a plurality of predictions; aggregating the predictions to derive a decision on a final classification of the new tuple; validating a first hyperplane classifier associated with a granule by attempting to classify a plurality of tuples from the training dataset which were not included in the granule; generating a hyperplane classifier effectiveness level based upon the validation of the first hyperplane classifier against tuples from the training dataset which were not included in the granule; determining whether the hyperplane classifier effectiveness level exceeds a threshold effectiveness level; and in response to determining that the hyperplane classifier effectiveness level does not exceed the threshold effectiveness level; removing the first hyperplane classifier; and requesting a new training dataset for use in identifying new hyperplane classifiers, wherein the new training dataset does not include data from the training dataset.
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23. A computer program product, encoded on a computer-readable medium, operable to cause one or more processors to perform operations comprising:
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receiving a training dataset comprising a plurality of tuples and a plurality of attributes for each of the tuples; deriving a plurality of granules from the training dataset, each granule comprising a plurality of sample tuples and a plurality of sample attributes, wherein for each of the plurality of granules; the plurality of sample tuples is randomly selected from among the plurality of tuples with replacement; and the plurality of sample attributes is randomly selected from among the plurality of attributes without replacement; processing the granules using a support vector machine process to identify a hyperplane classifier associated with each of the granules; predicting a classification of a new tuple using each of the hyperplane classifiers to produce a plurality of predictions; aggregating the predictions to derive a decision on a final classification of the new tuple; validating a first hyperplane classifier associated with a granule by attempting to classify a plurality of tuples from the training dataset which were not included in the granule; generating a hyperplane classifier effectiveness level based upon the validation of the first hyperplane classifier against tuples from the training dataset which were not included in the granule; determining whether the hyperplane classifier effectiveness level exceeds a threshold effectiveness level; and in response to determining that the hyperplane classifier effectiveness level does not exceed the threshold effectiveness level; removing the first hyperplane classifier; and requesting a new training dataset for use in identifying new hyperplane classifiers, wherein the new training dataset does not include data from the training dataset.
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Specification