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

Simon Fong,Yunsick Sung,Kelvin K. L. Wong,Jinyan Li,Kyungeun Cho,Raymond Wong

doi:10.1186/s13040-016-0117-1

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.

Highlights

An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes
Classification is a machine learning technique that integrates the complex relationships between the input variables and the target classes of some biomedical data
Machine learning from biomedical data encounters several difficulties, mainly because these datasets are characterised by incompleteness, incorrectness, inexactness and sparseness

Summary

Introduction

An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Samples from the stimulating class are rare in a population, such as medical anomalies, positive clinical tests, and particular diseases. Machine learning from biomedical data encounters several difficulties, mainly because these datasets are characterised by incompleteness (missing values), incorrectness (collection error or noise in the data), inexactness (data retrieved from incorrect sources) and sparseness (too few records available). Another subtle problem that transcends the integrity of the data is an imbalanced class distribution; that is, there are too few target data that the users are interested in amongst too much ordinary data collected. Other examples are cancer genes in microarrays [1], abnormal sub-sequences in biosignal patterns [2], tiny cysts in mammograms of the biological imaging field [3] and the colony distribution and mutation of E. coli or yeast [4, 5], in addition to classification in the biomedical engineering field [6], etc

Methods

Results

Conclusion