ADAPTIVE SAMPLING FOR IMBALANCE MITIGATION AND DATASET SIZE REDUCTION IN MACHINE LEARNING

• Main Authors: CAI JINGXIAO, IDICULA SAM, YAKOVLEV ANATOLY, VARADARAJAN VENKATANATHAN, AGARWAL NIPUN, AGRAWAL SANDEEP
• Format: Patent
• Language: Chinese; English
• Published: 10.12.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

According to an embodiment, a method includes generating a first dataset sample from a dataset, calculating a first validation score for the first dataset sample and a machine learning model, and determining whether a difference in validation score between the first validation score and a second validation score satisfies a first criteria. If the difference in validation score does not satisfy the first criteria, the method includes generating a second dataset sample from the dataset. If the difference in validation score does satisfy the first criteria, the method includes updating a convergence value and determining whether the updated convergence value satisfies a second criteria. If the updated convergence value satisfies the second criteria, the method includes returning the first dataset sample. If the updated convergence value does not satisfy the second criteria, the method includes generating the second dataset sample from the dataset. 根据实施例，一种方法包括从数据集生成第一数据集样本，计算第一数据集样本和机器学习模型的第一验证分数，以及确定第一验证分数和第二验证