NDLI: Improving the classification performance of biological imbalanced datasets by swarm optimization algorithms

Content Provider	Springer Nature Link
Author	Li, Jinyan Fong, Simon Mohammed, Sabah Fiaidhi, Jinan
Copyright Year	2015
Abstract	Classification which is a popular supervised machine learning method has many applications in computational biology, where data samples are automatically categorized into predefined labels with the aid of data mining. Often the training samples contain very few instances of interest (e.g., medical anomalies, rare disease in a population, and unusual syndromes, etc.), but many normal instances. Such imbalanced ratio of data distributions among the target labels hampers the efficacy of classification algorithms, because the induced model has not been trained with sufficient amount of instances of the interesting label(s), but overwhelmed with ordinary training records. Traditional remedies attempt to rebalance the data distributions of the target classes, by inflating the interesting instances artificially, reducing the majority of the common instances or a combination of both. Though the fundamental concept is effective, there is no clear guideline on how to strike a balance between fabricating the rare samples and reducing the norms, with the purpose of maximizing the classification accuracy. In this paper, an optimization model using different swarm strategies (Bat-inspired algorithm and PSO) is proposed for adaptively balancing the increase/decrease of the class distribution, depending on the properties of the biological datasets. The optimization is extended for achieving the highest possible accuracy and Kappa statistics at the same time as well. The optimization model is tested on five imbalanced medical datasets, which are sourced from lung surgery logs and virtual screening of bioassay data. Computer simulation results show that the proposed optimization model outperforms other class balancing methods in medical data classification.
Starting Page	3708
Ending Page	3728
Page Count	21
File Format	PDF
ISSN	09208542
Journal	The Journal of Supercomputing
Volume Number	72
Issue Number	10
e-ISSN	15730484
Language	English
Publisher	Springer US
Publisher Date	2015-11-16
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Imbalanced biological data Medical classification Swarm algorithm Parameter optimization Programming Languages, Compilers, Interpreters Processor Architectures Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Information Systems Hardware and Architecture Software

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