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US 2024/0256950 A1Published application
Systems and methods for determination, description, and use of feature sets for machine learning classification systems, including electronic messaging systems employing machine learning classification
AssigneeZix Corporation
InventorPeter Gallagher McNeil
Life of this patent
Estimating the term…
Abstract
Systems and methods for dynamically defining features for machine learning models in classification systems are disclosed. Such systems and methods may be usefully applied to train and utilize machine learning models in a variety of contexts, including email classification systems.
Claims21
A method for feature creation in machine learning models, comprising:
- processing each of a training documents of a training corpus, the training documents associated with a class label, wherein the processing of each training document comprises:
- determining a first fragment from the training document;
- determining a first feature descriptor for the first fragment, the first feature descriptor determined based on attributes of the first fragment according to an abstract naming schema that defines portions of the first feature descriptor based on those attributes;
- determining a value associated with the first feature descriptor based on the first fragment;
- adding the first feature descriptor to a feature descriptor catalog when the determined first feature descriptor is not in the feature descriptor catalog;
- and
- adding the value for the first feature to training data in association with the first feature descriptor and the training document;
- training the machine learning model based on the training data such that features of the trained machine learning model comprise feature descriptors determined for fragments of the training document of the training corpus, wherein the training data comprises the values for feature descriptors for the training documents and the class label associated with the training documents;
- and
- providing the feature descriptor catalog and the trained machine learning model for use in classification of documents.
7 more elements
A system for training and use of machine learning models in document classification, comprising:
- a processor;
- a non-transitory computer-readable medium comprising instructions for:
- processing each of a training documents of a training corpus, the training documents associated with a class label, wherein the processing of each training document comprises:
- determining a first fragment from the training document;
- determining a first feature descriptor for the first fragment, the first feature descriptor determined based on attributes of the first fragment according to an abstract naming schema that defines portions of the first feature descriptor based on those attributes;
- determining a value associated with the first feature descriptor based on the first fragment;
- adding the first feature descriptor to a feature descriptor catalog when the determined first feature descriptor is not in the feature descriptor catalog;
- and
- adding the value for the first feature to training data in association with the first feature descriptor and the training document;
- training the machine learning model based on the training data such that features of the trained machine learning model comprise feature descriptors determined for fragments of the training document of the training corpus, wherein the training data comprises the values for feature descriptors for the training documents and the class label associated with the training documents;
- and
- providing the feature descriptor catalog and the trained machine learning model for use in classification of documents.
9 more elements
A non-transitory computer readable medium, comprising instructions for:
- processing each of a training documents of a training corpus, the training documents associated with a class label, wherein the processing of each training document comprises:
- determining a first fragment from the training document;
- determining a first feature descriptor for the first fragment, the first feature descriptor determined based on attributes of the first fragment according to an abstract naming schema that defines portions of the first feature descriptor based on those attributes;
- determining a value associated with the first feature descriptor based on the first fragment;
- adding the first feature descriptor to a feature descriptor catalog when the determined first feature descriptor is not in the feature descriptor catalog;
- and
- adding the value for the first feature to training data in association with the first feature descriptor and the training document;
- training the machine learning model based on the training data such that features of the trained machine learning model comprise feature descriptors determined for fragments of the training document of the training corpus, wherein the training data comprises the values for feature descriptors for the training documents and the class label associated with the training documents;
- and
- providing the feature descriptor catalog and the trained machine learning model for use in classification of documents.
7 more elements
Dependencies are inferred from the claim text.