Classification based on imbalanced dataset

A method comprising:

• generating, by one or more processors, multiple machine learning models respectively based on multiple positive samples included in an imbalanced dataset, wherein an amount of the multiple positive samples is below an amount of multiple negative samples that are included in the imbalanced dataset, and wherein each of the multiple positive samples and the multiple negative samples includes respective parameters;
• inputting a first new sample into the multiple machine learning models to respectively produce predictions that classify the first new sample as a positive type or a negative type;
• responsive to determining that at least one of the predictions indicates the positive type, generating, by the one or more processors, a model set including at least one of the machine learning models whose prediction indicated the positive type;
• determining, by the one or more processors, an influential parameter group associated with a first machine learning model in the model set, wherein the influential parameter group causes the first machine learning model to classify an associated one of the multiple positive samples as the positive type;
• identifying, by the one or more processors, a target first influential parameter group for the first new sample with respect to the first machine learning model, wherein the target first influential parameter group causes the first machine learning model to classify the first new sample as the positive type;
• removing, by the one or more processors, the first machine learning model from the model set in response to a difference between the influential parameter group and the first influential parameter group exceeding a predefined threshold value, wherein the removing changes the model set to an updated model set and the updated model set contains a smaller number of machine learning models than the model set contains;
• and
• classifying, by the one or more processors, the first new sample based on the updated model set.

A computer system comprising:

• a computer processor;
• and
• a computer-readable memory unit that stores instructions that cause the computer processor to perform operations comprising:
• generating multiple machine learning models respectively based on multiple positive samples included in an imbalanced dataset, wherein an amount of the multiple positive samples is below an amount of multiple negative samples that are included in the imbalanced dataset, and wherein each of the multiple positive samples and the multiple negative samples includes respective parameters;
• identifying from the respective parameters multiple influential parameter groups respectively for the multiple positive samples, wherein the multiple influential parameter groups respectively cause the multiple machine learning models to classify an associated one of the multiple positive samples as a positive type;
• and
• determining a model set that includes at least some of the machine learning models;
• and
• inputting a first new sample into the model set such that the model set, in response classifies the first new sample as a positive type or a negative type, wherein the model set compares the multiple influential parameter groups to target influential parameter groups as part of the classifying.

A computer program product comprising:

• a computer readable storage medium;
• and
• program instructions stored in the computer readable storage medium, the program instructions executable by a processor to cause the processor to perform operations comprising:
• generating multiple machine learning models respectively based on multiple positive samples included in an imbalanced dataset, wherein an amount of the multiple positive samples is below an amount of multiple negative samples that are included in the imbalanced dataset, and wherein each of the multiple positive samples and the multiple negative samples includes respective parameters;
• identifying from the respective parameters multiple influential parameter groups respectively for the multiple positive samples, wherein the multiple influential parameter groups respectively cause the multiple machine learning models to classify an associated one of the multiple positive samples as a positive type;
• determining a model set that includes at least some of the multiple machine learning models;
• and
• inputting a first new sample into the model set such that the model set, in response, classifies the first new sample as a positive type or a negative type, wherein the model set compares the multiple influential parameter groups to target influential parameter groups as part of the classifying.