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US 12,762,475 B2Granted
Systems and methods for bagging ensemble classifiers for imbalanced big data
AssigneeCapital One Services, LLC
InventorMichael Langford
Life of this patent
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Abstract
Disclosed embodiments may include a method for bagging ensemble classifiers for imbalanced big data. The system may receive user input comprising a number of machine learning base models to generate. The system may generate the machine learning base models based on the user input. Iteratively for each machine learning base model of the machine learning base models until all machine learning base models are trained, the system may: determine a chunk for a machine learning base model of the machine learning base models, wherein the chunk comprises all minority cases from training data and a plurality of majority cases from the training data and train the machine learning base model with the chunk.
Claims20
A system, comprising:
- one or more processors;
- and
- a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to:
- receive a first dataset;
- store a minority portion of the first dataset as testing data and a remaining portion of the first dataset as training data;
- separate the training data into majority cases and minority cases;
- receive user input comprising a number of machine learning base models to generate;
- generate a plurality of machine learning base models corresponding to the number based on the user input;
- iteratively for each machine learning base model of the plurality of machine learning base models until all machine learning base models of the plurality of machine learning base models are trained:
- determine a chunk for a machine learning base model of the plurality of machine learning base models, wherein the chunk comprises all minority cases from the training data and a subset of majority cases from the training data that is unique from other chunks associated with other machine learning base models of the plurality of machine learning base models;
- and
- train the machine learning base model with the chunk;
- and
- validate the plurality of machine learning base models using the testing data,
- wherein at least a first subset of majority cases of a first chunk associated with a first machine learning base model and a second subset of majority cases of a second chunk associated with a second machine learning base model comprise an overlap of one or more majority cases such that the first chunk and second chunk both comprise the one or more majority cases.
12 more elements
A system, comprising:
- one or more processors;
- and
- a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to:
- receive user input comprising a number of machine learning base models to generate;
- generate a plurality of machine learning base models corresponding to the number based on the user input;
- iteratively for each machine learning base model of the plurality of machine learning base models until all machine learning base models of the plurality of machine learning base models are trained:
- determine a chunk for a machine learning base model of the plurality of machine learning base models, wherein the chunk comprises all minority cases from training data and a subset of majority cases from the training data that is unique from other chunks associated with other machine learning base models of the plurality of machine learning base models;
- and
- train the machine learning base model with the chunk,
- wherein at least a first subset of majority cases of a first chunk associated with a first machine learning base model and a second subset of majority cases of a second chunk associated with a second machine learning base model comprise an overlap of one or more majority cases such that the first chunk and second chunk both comprise the one or more majority cases.
7 more elements
A system, comprising:
- one or more processors;
- and
- a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to:
- receive training data separated into majority cases and minority cases;
- generate a plurality of machine learning base models based on an amount of majority cases and minority cases;
- iteratively for each machine learning base model of the plurality of machine learning base models until all machine learning base models of the plurality of machine learning base models are trained:
- determine a chunk for a machine learning base model of the plurality of machine learning base models, wherein the chunk comprises all minority cases from the training data and a subset of majority cases from the training data that is unique from other chunks associated with other machine learning base models of the plurality of machine learning base models;
- and
- train the machine learning base model with the chunk,
- wherein at least a first subset of majority cases of a first chunk associated with a first machine learning base model and a second subset of majority cases of a second chunk associated with a second machine learning base model comprise an overlap of one or more majority cases such that the first chunk and second chunk both comprise the one or more majority cases.
7 more elements
Dependencies are inferred from the claim text.