TY - JOUR AU - Li, Jinyan AU - Fong, Simon AU - Sung, Yunsick AU - Cho, Kyungeun AU - Wong, Raymond AU - Wong, Kelvin K. L. PY - 2016 DA - 2016/12/01 TI - Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification JO - BioData Mining SP - 37 VL - 9 IS - 1 AB - An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Often in biomedical applications, samples from the stimulating class are rare in a population, such as medical anomalies, positive clinical tests, and particular diseases. Although the target samples in the primitive dataset are small in number, the induction of a classification model over such training data leads to poor prediction performance due to insufficient training from the minority class. SN - 1756-0381 UR - https://doi.org/10.1186/s13040-016-0117-1 DO - 10.1186/s13040-016-0117-1 ID - Li2016 ER -