Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classificationReport as inadecuate




Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification - Download this document for free, or read online. Document in PDF available to download.

BioData Mining

, 9:37

First Online: 01 December 2016Received: 13 May 2016Accepted: 21 November 2016

Abstract

BackgroundAn 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.

ResultsIn 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.

ConclusionsOur 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.

KeywordsImbalanced dataset Swarm optimisation Under-sampling SMOTE Dynamic Multi-objective Classification Biomedical data  Download fulltext PDF



Author: Jinyan Li - Simon Fong - Yunsick Sung - Kyungeun Cho - Raymond Wong - Kelvin K. L. Wong

Source: https://link.springer.com/







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