A suite of swarm dynamic multi-objective algorithms for rebalancing extremely imbalanced datasets
Li,Jinyan1,2; Fong,Simon1; Wong,Raymond K.3; Mohammed,Sabah4; Fiaidhi,Jinan4; Sung,Yunsick5
Source PublicationApplied Soft Computing Journal
AbstractImbalanced datasets can be found in a number of fields; they are commonly regarded as big data because of their sheer volume and high attribute dimensions. As the name suggests, imbalanced big datasets come with an extremely imbalanced ratio between the amount of major class and minority class samples. Traditional methods: have been attempted but still cannot fully, effectively, and reliably solve the imbalanced class classification problem, especially when the distribution of the classes is exceedingly imbalanced. In this paper, we propose a collection of algorithms to solve the problem of imbalanced datasets in binary data classification. Most traditional methods: rebalance the imbalanced dataset merely by matching the data quantities of the two classes. Our proposed algorithms, which take the form of a suite of variants, focus on guaranteeing the credibility of the classification model and reaching the greatest possible accuracy by dynamically rebalancing the training dataset with multi-objective swarm intelligence optimisation. The new algorithms are extended from those we proposed earlier, which had a single objective – first find a set of solutions that satisfy the Kappa criterion, then search for the solution in the set that offers the highest accuracy. Two main modifications are made in the new algorithms. Multi-objective optimisation is aimed at finding a solution that satisfies several criteria at the same time, such as accuracy and identifying a list of credibility indicators. The other enhancement is the incremental operation of the multi-objective optimisation. Incremental optimisation is imperative for processing data feeds that may arrive in a streaming manner. Instead of waiting for the full data archive to be available before optimisation, incremental optimisation rebalances the data feed segment by segment on the fly. The experimental results from the suite of proposed algorithms show that they can effectively attain better and more stable performances from the classification model and are accompanied by much greater credibility than the other five traditional methods when imbalanced datasets are used as training datasets for inducing a classifier.
KeywordBig highly imbalanced dataset Binary classification Dynamic multi-objective Rebalancing algorithm Swarm intelligence algorithms
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorFong,Simon
Affiliation1.Department of Computer and Information Science,University of Macau,Taipa,Macao
2.Big Data PDU,Huawei Software Theologies,CO. LTD,Nanjing,China
3.School of Computer Science and Engineering,University of New South Wales,Sydney,2000,Australia
4.Department of Computer Science,Lakehead University,Thunder Bay,Canada
5.Dept of Multimedia Engineering,Dongguk-Seoul,Seoul,Republic of Korea 30, Pildong-ro 1gil, Jung-gu,04602,South Korea
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
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GB/T 7714
Li,Jinyan,Fong,Simon,Wong,Raymond K.,et al. A suite of swarm dynamic multi-objective algorithms for rebalancing extremely imbalanced datasets[J]. Applied Soft Computing Journal,2018,69:784-805.
APA Li,Jinyan,Fong,Simon,Wong,Raymond K.,Mohammed,Sabah,Fiaidhi,Jinan,&Sung,Yunsick.(2018).A suite of swarm dynamic multi-objective algorithms for rebalancing extremely imbalanced datasets.Applied Soft Computing Journal,69,784-805.
MLA Li,Jinyan,et al."A suite of swarm dynamic multi-objective algorithms for rebalancing extremely imbalanced datasets".Applied Soft Computing Journal 69(2018):784-805.
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