title: Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  In this paper, we use a novel class-balancing method named adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique (ASCB_DmSMOTE) to solve this imbalanced dataset problem, which is common in biomedical applications. The proposed method combines under-sampling and over-sampling into a swarm optimisation algorithm. It adaptively selects suitable parameters for the rebalancing algorithm to find the best solution. Compared with the other versions of the SMOTE algorithm, significant improvements, which include higher accuracy and credibility, are observed with ASCB_DmSMOTE.
  
  
  CONCLUSIONS
  Our proposed method tactfully combines two rebalancing techniques together. It reasonably re-allocates the majority class in the details and dynamically optimises the two parameters of SMOTE to synthesise a reasonable scale of minority class for each clustered sub-imbalanced dataset. The proposed methods ultimately overcome other conventional methods and attains higher credibility with even greater accuracy of the classification model.
authors.affiliation: Department of Computer and Information Science, University of Macau, Taipa, Macau, S.A.R. China.
authors.forename: Jinyan
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Computer and Information Science, University of Macau, Taipa, Macau, S.A.R. China.
authors.forename: Simon
authors.initials: S
authors.lastname: Fong
authors.affiliation: Computer Engineering Division, Keimyung University, Daegu, South Korea.
authors.forename: Yunsick
authors.initials: Y
authors.lastname: Sung
authors.affiliation: Department of Multimedia Engineering, College of Engineering, Dongguk University, Dongdaeipgu, South Korea.
authors.forename: Kyungeun
authors.initials: K
authors.lastname: Cho
authors.affiliation: School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2000 Australia.
authors.forename: Raymond
authors.initials: R
authors.lastname: Wong
authors.affiliation: Centre for Biomedical Engineering, School of Electrical & Electronic Engineering, University of Adelaide, Adelaide, Australia.|School of Medicine, Western Sydney University, Campbelltown, Sydney Australia.
authors.forename: Kelvin K L
authors.initials: KKL
authors.lastname: Wong
keywords: Biomedical data; Classification; Dynamic Multi-objective; Imbalanced dataset; SMOTE; Swarm optimisation; Under-sampling