Multi-modality classification for one-class classification in social networks
First Claim
1. A classification method comprising:
- receiving a set of objects, the objects comprising electronic mail messages, a subset of the objects having class labels;
extracting features associated with each of a first and a second modality, the second of the modalities being a social-network modality in which social network features are extracted from a social network implicit in the electronic mail messages, from objects in the set of objects and generating a representation of each object based on its extracted features;
training a classifier system based on the class labels of the subset of the set of objects and on the representations generated for each of the modalities; and
predicting labels for unlabeled objects in the set of objects using the trained classifier system.
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Abstract
A classification apparatus, method, and computer program product for multi-modality classification are disclosed. For each of a plurality of modalities, the method includes extracting features from objects in a set of objects. The objects include electronic mail messages. A representation of each object for that modality is generated, based on its extracted features. At least one of the plurality of modalities is a social network modality in which social network features are extracted from a social network implicit in the set of electronic mail messages. A classifier system is trained based on class labels of a subset of the set of objects and on the representations generated for each of the modalities. With the trained classifier system, labels are predicted for unlabeled objects in the set of objects.
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Citations
22 Claims
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1. A classification method comprising:
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receiving a set of objects, the objects comprising electronic mail messages, a subset of the objects having class labels; extracting features associated with each of a first and a second modality, the second of the modalities being a social-network modality in which social network features are extracted from a social network implicit in the electronic mail messages, from objects in the set of objects and generating a representation of each object based on its extracted features; training a classifier system based on the class labels of the subset of the set of objects and on the representations generated for each of the modalities; and predicting labels for unlabeled objects in the set of objects using the trained classifier system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A classification apparatus comprising:
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memory which stores instructions for performing a method comprising; for each of first and second modalities; extracting features from objects in a set of objects, the objects comprising electronic mail messages, and generating a representation of each object based on its extracted features; the first of the modalities being a text-based modality, the second of the modalities being a social-network modality in which social network features are extracted from a social network implicit in the electronic mail messages; training a classifier system based on class labels of a subset of the set of objects and on the representations generated for each of the modalities; and with the trained classifier system, predicting labels for unlabeled objects in the set of objects; and a processor, in communication with the memory for executing the instructions.
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16. A non-transitory computer program product encoding instructions, which when executed by a computer, perform a method comprising:
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receiving a set of objects, the objects comprising electronic mail messages, a subset of the objects having class labels; extracting features associated with each of a first and a second modality, the second of the modalities being a social-network modality in which social network features are extracted from a social network implicit in the electronic mail messages, from objects in the set of objects and generating a representation of each object based on its extracted features; training a classifier system based on the class labels of the subset of the set of objects and on the representations generated for each of the modalities; and predicting labels for unlabeled objects in the set of objects using the trained classifier system.
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17. A classification apparatus comprising:
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an input for receiving a set of objects, the objects comprising electronic mail messages, a subset of the objects having class labels; a first feature extractor which extracts text-based features from objects in the set of objects; a second feature extractor which extracts social network-based features from the objects in the set of objects; a classifier system, which predicts labels for unlabeled objects in the set of objects based on the class labels of the subset of objects having class labels and the extracted text-based and social network-based features; and a processor which executes the classifier system.
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18. A classification method comprising:
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receiving a set of objects, the objects comprising electronic mail messages, an initial subset of the objects being positively labeled with respect to a class; extracting features associated with each of a first and a second modality from objects in the set of objects and generating a representation of each object based on its extracted features; training a one-class classifier system based on class labels of the subset of the set of objects and on the representations generated for each of the first and second modalities, the training including, for each of the modalities; based on the initial set of objects that are positively labeled with respect to the class, generating an initial hypothesis which predicts negative labels for a subset of unlabeled objects in the set of objects, and iteratively generating a new hypothesis in which a new boundary between representations of objects predicted as having negative labels and representations of objects predicted as having positive labels converges towards an original boundary between the representations of the initial positively labeled objects and the rest of the objects in the set; and predicting labels for unlabeled objects in the set of objects using the trained classifier system. - View Dependent Claims (19, 20, 21, 22)
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Specification