CBR-PSO: cost-based rough particle swarm optimization approach for high-dimensional imbalanced problems
| Autor: | Emel Kızılkaya Aydoğan, Yılmaz Delice, Mihrimah Özmen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science Particle swarm optimization 02 engineering and technology High dimensional computer.software_genre Statistical classification ComputingMethodologies_PATTERNRECOGNITION 020901 industrial engineering & automation Artificial Intelligence Robustness (computer science) Algorithmic efficiency 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Rough set Data mining computer Software |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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