HILL-CLIMBING FEATURE SELECTION WITH MAX-RELEVANCY AND MINIMUM REDUNDANCY CRITERIA
First Claim
1. An information processing system for selecting features from a feature space, the information processing system comprising:
- a memory;
a processor communicatively coupled to the memory; and
a feature selection module coupled to the memory and the processor, wherein the feature selection module is configured to perform a method comprising;
selecting, by a processor, a candidate feature set of k′
features from at least one set of features based on maximum relevancy and minimum redundancy (MRMR) criteria;
identifying a target feature set of k features from the candidate feature set, where k′
>
k;
iteratively updating each of a plurality of features in the target feature set with each of a plurality of k′
−
k features from the candidate feature set;
maintaining, for at least one iterative update, the feature from the plurality of k′
−
k features in the target feature set based on a current MRMR score of the target feature set satisfying a threshold; and
storing, after a given number of iterative updates, the target feature set as a top-k feature set of the at least one set of features.
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Accused Products
Abstract
Various embodiments select features from a feature space. In one embodiment a candidate feature set of k′ features is selected from at least one set of features based on maximum relevancy and minimum redundancy (MRMR) criteria. A target feature set of k features is identified from the candidate feature set, where k′>k. Each a plurality of features in the target feature set is iteratively updated with each of a plurality of k′−k features from the candidate feature set. The feature from the plurality of k′−k features is maintained in the target feature set, for at least one iterative update, based on a current MRMR score of the target feature set satisfying a threshold. The target feature set is stored as a top-k feature set of the at least one set of features after a given number of iterative updates.
19 Citations
13 Claims
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1. An information processing system for selecting features from a feature space, the information processing system comprising:
-
a memory; a processor communicatively coupled to the memory; and a feature selection module coupled to the memory and the processor, wherein the feature selection module is configured to perform a method comprising; selecting, by a processor, a candidate feature set of k′
features from at least one set of features based on maximum relevancy and minimum redundancy (MRMR) criteria;identifying a target feature set of k features from the candidate feature set, where k′
>
k;iteratively updating each of a plurality of features in the target feature set with each of a plurality of k′
−
k features from the candidate feature set;maintaining, for at least one iterative update, the feature from the plurality of k′
−
k features in the target feature set based on a current MRMR score of the target feature set satisfying a threshold; andstoring, after a given number of iterative updates, the target feature set as a top-k feature set of the at least one set of features. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A non-transitory computer program product for selecting features from a feature space, the computer program product comprising:
a storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising; selecting, by a processor, a candidate feature set of k′
features from at least one set of features based on maximum relevancy and minimum redundancy (MRMR) criteria;identifying a target feature set of k features from the candidate feature set, where k′
>
k;iteratively updating each of a plurality of features in the target feature set with each of a plurality of k′
−
k features from the candidate feature set;maintaining, for at least one iterative update, the feature from the plurality of k′
−
k features in the target feature set based on a current MRMR score of the target feature set satisfying a threshold; andstoring, after a given number of iterative updates, the target feature set as a top-k feature set of the at least one set of features. - View Dependent Claims (8, 9, 10, 11, 12, 13)
Specification