sansa
Synthetic Data Generation for Imbalanced Learning in 'R'
Machine learning is widely used in information-systems design. Yet, training algorithms on imbalanced datasets may severely affect performance on unseen data. For example, in some cases in healthcare, financial, or internet-security contexts, certain sub-classes are difficult to learn because they are underrepresented in training data. This 'R' package offers a flexible and efficient solution based on a new synthetic average neighborhood sampling algorithm ('SANSA'), which, in contrast to other solutions, introduces a novel “placement” parameter that can be tuned to adapt to each datasets unique manifestation of the imbalance. More information about the algorithm's parameters can be found at Nasir et al. (2022)
- Version0.0.1
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?No
- Last release08/23/2022
Documentation
Team
Murtaza Nasir
Ali Dag
Show author detailsRolesContributorSerhat Simsek
Show author detailsRolesContributorAnton Ivanov
Show author detailsRolesContributorAsil Oztekin
Show author detailsRolesThesis advisor
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