CBR-PSO: cost-based rough particle swarm optimization approach for high-dimensional imbalanced problems

Datasets, which have a considerably larger number of attributes compared to samples, face a serious classification challenge. This issue becomes even harder when such high-dimensional datasets are also imbalanced. Recently, such datasets have attracted the interest of both industry and academia and...

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Bibliographic Details
Published in:Neural computing & applications 2019-10, Vol.31 (10), p.6345-6363
Main Authors: Aydogan, Emel Kızılkaya, Ozmen, Mihrimah, Delice, Yılmaz
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Cost analysis
Data Mining and Knowledge Discovery
Datasets
Image Processing and Computer Vision
Original Article
Particle swarm optimization
Probability and Statistics in Computer Science
Reduction
Rough set models
Set theory
Statistical analysis
Statistical methods
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Summary:Datasets, which have a considerably larger number of attributes compared to samples, face a serious classification challenge. This issue becomes even harder when such high-dimensional datasets are also imbalanced. Recently, such datasets have attracted the interest of both industry and academia and thereby have become a very attractive research area. In this paper, a new cost-sensitive classification method, the CBR-PSO, is presented for such high-dimensional datasets with different imbalance ratios and number of classes. The CBR-PSO is based on particle swarm optimization and rough set theory. The robustness of the algorithm is based on the simultaneously applying attribute reduction and classification; in addition, these two stages are also sensitive to misclassification cost. Algorithm efficiency is examined in publicly available datasets and compared to well-known attribute reduction and cost-sensitive classification algorithms. The statistical analysis and experiments showed that the CBR-PSO can be better than or comparable to the other algorithms, in terms of MAUC values.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-018-3469-2