Diversity-aware weighted majority vote classifier decision making on for imbalanced datasets

A system configured to execute an ensemble learning based method, by one or more computer processors of the system, for a binary classification task on an imbalanced dataset, the imbalanced dataset comprising a minority class comprising positive samples and a majority class comprising negative samples, the binary classification being applied in a second stage of diagnosis done in two stages to eliminate those with false positives on a first stage, the ensemble learning based method comprising:

• generatively oversampling minority class examples included in the imbalanced dataset by synthetically generating the minority class examples using computer-implemented algorithms, thereby generating a modified dataset D′ of the minority class;
• using the modified dataset D′ generated dataset to generate a plurality of subsamples in a random and stratified way such that each subsample, of the subsamples, contains random examples while preserving a class-distribution ratio of the modified dataset D′, and learning a base classifier on each of the subsamples using machine learning algorithms executed on the one or more processors to determine a plurality of base classifiers;
• learning a diversity-aware weighted majority vote classifier by combining outputs of the base classifiers;
• and
• executing the binary classification task by applying the diversity-aware weighted majority vote classifier to the imbalanced dataset,
• wherein each of the base classifiers is assigned a weight in such a way that a diversity between the base classifiers on the positive samples only is minimized,
• wherein the binary classification task outputs a final prediction indicating samples as ones of the positive samples and the negative samples, and
• wherein the diversity-aware weighted majority vote classifier is configured to process input data and generate classification outputs with low false positive rates for the second stage diagnosis.

A non-transitory computer readable medium having, stored thereon, instructions for performing an ensemble learning based method executed by one or more computer processors for a binary classification task on an imbalanced dataset, the imbalanced dataset comprising a minority class comprising positive samples and a majority class comprising negative samples, the binary classification being applied in a second stage of diagnosis done in two stages to eliminate those with false positives on a first stage, the ensemble learning based method comprising:

• generatively oversampling minority class examples included in the imbalanced dataset by synthetically generating the minority class examples using computer-implemented algorithms, thereby generating a modified dataset D′ of the minority class;
• using the modified dataset D′ to generate a plurality of subsamples in a random and stratified way such that each subsample, of the subsamples, contains random examples while preserving a class-distribution ratio of the modified dataset D′, and learning a base classifier on each of the subsamples using machine learning algorithms executed on the one or more processors to determine a plurality of base classifiers;
• learning a diversity-aware weighted majority vote classifier by combining the outputs of the base classifiers;
• and
• executing the binary classification task by applying the diversity-aware weighted majority vote classifier to the imbalanced dataset,
• wherein each of the base classifiers is assigned a weight in such a way that a diversity between the base classifiers on the positive samples only is minimized,
• wherein the binary classification task outputs a final prediction indicating samples as ones of the positive samples and the negative samples, and
• wherein the diversity-aware weighted majority vote classifier is configured to process input data and generate classification outputs with low false positive rates for the second stage diagnosis.