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Dan Min He

from Little Neck, NY
Age ~33
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Also known as:
Dana M He, Danmin He, Dan Ahe
Related to:
Barbara A Longo, 82Meizhen He, 54Tiffany Phillips, 54Meizhen He
Connected to:
Ai L He, 46Alina He, 37Angel M He, 34Anli He, 47Benjamin B He, 51Brian He, 32Candice J He, 33Charles C He, 62Chuan He, 51Chunxiang He, 47

Dan He Phones & Addresses

  • Little Neck, NY
  • Brooklyn, NY

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Dan He Photo 1

Dan He

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Location:
United States

Publications

Us Patents

Feature Selection For Efficient Epistasis Modeling For Phenotype Prediction

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US Patent:
20140207427, Jul 24, 2014
Filed:
Jan 21, 2013
Appl. No.:
13/745914
Inventors:
- Armonk NY, US
Dan HE - Ossining NY, US
Laxmi P. PARIDA - Mohegan Lake NY, US
Assignee:
INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY
International Classification:
G06F 19/12
US Classification:
703 2
Abstract:
Various embodiments select markers for modeling epistasis effects. In one embodiment, a processor receives a set of genetic markers and a phenotype. A relevance score is determined with respect to the phenotype for each of the set of genetic markers. A threshold is set based on the relevance score of a genetic marker with a highest relevancy score. A relevance score is determined for at least one genetic marker in the set of genetic markers for at least one interaction between the at least one genetic marker and at least one other genetic marker in the set of genetic markers. The at least one interaction is added to a top-k feature set based on the relevance score of the at least one interaction satisfying the threshold.

Feature Selection For Efficient Epistasis Modeling For Phenotype Prediction

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US Patent:
20140207436, Jul 24, 2014
Filed:
Sep 18, 2013
Appl. No.:
14/030743
Inventors:
- Armonk NY, US
Dan HE - Ossining NY, US
Laxmi P. PARIDA - Mohegan Lake NY, US
Assignee:
INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY
International Classification:
G06F 19/18
G06F 19/12
US Classification:
703 11
Abstract:
Various embodiments select markers for modeling epistasis effects. In one embodiment, a processor receives a set of genetic markers and a phenotype. A relevance score is determined with respect to the phenotype for each of the set of genetic markers. A threshold is set based on the relevance score of a genetic marker with a highest relevancy score. A relevance score is determined for at least one genetic marker in the set of genetic markers for at least one interaction between the at least one genetic marker and at least one other genetic marker in the set of genetic markers. The at least one interaction is added to a top-k feature set based on the relevance score of the at least one interaction satisfying the threshold.

Transductive Feature Selection With Maximum-Relevancy And Minimum-Redundancy Criteria

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US Patent:
20140207711, Jul 24, 2014
Filed:
Jan 21, 2013
Appl. No.:
13/745930
Inventors:
- Armonk NY, US
Dan HE - Ossining NY, US
Laxmi P. PARIDA - Mohegan Lake NY, US
Irina RISH - Rye Brook NY, US
Assignee:
INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY
International Classification:
G06N 99/00
US Classification:
706 12
Abstract:
Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. The set of training samples includes a set of features and a class value. The set of test samples includes the set of features absent the class value. A relevancy with respect to the class value is determined for each of a plurality of unselected features based on the set of training samples. A redundancy with respect to one or more of the set of features is determined for each of the plurality of unselected features in the first set of features based on the set of training samples and the set of test samples. A set of features is selected from the plurality of unselected features based on the relevancy and the redundancy determined for each of the plurality of unselected features.

Transductive Feature Selection With Maximum-Relevancy And Minimum-Redundancy Criteria

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US Patent:
20140207713, Jul 24, 2014
Filed:
Sep 18, 2013
Appl. No.:
14/030708
Inventors:
- Armonk NY, US
Dan HE - Ossining NY, US
Laxmi P. PARIDA - Mohegan Lake NY, US
Irina RISH - Rye Brook NY, US
Assignee:
INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY
International Classification:
G06N 99/00
US Classification:
706 12
Abstract:
Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. The set of training samples includes a set of features and a class value. The set of test samples includes the set of features absent the class value. A relevancy with respect to the class value is determined for each of a plurality of unselected features based on the set of training samples. A redundancy with respect to one or more of the set of features is determined for each of the plurality of unselected features in the first set of features based on the set of training samples and the set of test samples. A set of features is selected from the plurality of unselected features based on the relevancy and the redundancy determined for each of the plurality of unselected features.

Dynamic Feature Selection With Max-Relevancy And Minimum Redundancy Criteria

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US Patent:
20140207764, Jul 24, 2014
Filed:
Jan 21, 2013
Appl. No.:
13/745923
Inventors:
- Armonk NY, US
Dan He - Ossining NY, US
Laxmi P. Parida - Mohegan Lake NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 17/30
US Classification:
707723
Abstract:
Various embodiments select features from a feature space. In one embodiment a set of features and a class value are received. A redundancy score is obtained for a feature that was previously selected from the set of features. A redundancy score is determined, for each of a plurality of unselected features in the set of features, based on the redundancy score that has been obtained, and a redundancy between the unselected feature and the feature that was previously selected. A relevance to the class value is determined for each of the unselected features. A feature from the plurality of unselected features with a highest relevance to the class value and a lowest redundancy score is selected.

Dynamic Feature Selection With Max-Relevancy And Minimum Redundancy Criteria

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US Patent:
20140207765, Jul 24, 2014
Filed:
Sep 18, 2013
Appl. No.:
14/030720
Inventors:
- Armonk NY, US
Dan HE - Ossining NY, US
Laxmi P. PARIDA - Mohegan Lake NY, US
Assignee:
INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY
International Classification:
G06F 17/30
US Classification:
707723
Abstract:
Various embodiments select features from a feature space. In one embodiment a set of features and a class value are received. A redundancy score is obtained for a feature that was previously selected from the set of features. A redundancy score is determined, for each of a plurality of unselected features in the set of features, based on the redundancy score that has been obtained, and a redundancy between the unselected feature and the feature that was previously selected. A relevance to the class value is determined for each of the unselected features. A feature from the plurality of unselected features with a highest relevance to the class value and a lowest redundancy score is selected.

Hill-Climbing Feature Selection With Max-Relevancy And Minimum Redundancy Criteria

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US Patent:
20140207799, Jul 24, 2014
Filed:
Jan 21, 2013
Appl. No.:
13/745909
Inventors:
- Armonk NY, US
Dan HE - Ossining NY, US
Laxmi P. PARIDA - Mohegan Lake NY, US
Assignee:
INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY
International Classification:
G06F 17/30
US Classification:
707749
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.

Hill-Climbing Feature Selection With Max-Relevancy And Minimum Redundancy Criteria

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US Patent:
20140207800, Jul 24, 2014
Filed:
Sep 18, 2013
Appl. No.:
14/030806
Inventors:
- Armonk NY, US
Dan HE - Ossining NY, US
Laxmi P. PARIDA - Mohegan Lake NY, US
Assignee:
INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY
International Classification:
G06F 17/30
US Classification:
707749
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.
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Dan Min He from Little Neck, NY, age ~33 Get Report