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Streamwise feature selection on big data using noise resistant rough functional dependency | ||
| Journal of Mathematical Modeling | ||
| دوره 9، شماره 4، اسفند 2021، صفحه 677-690 اصل مقاله (374.61 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2021.18707.1603 | ||
| نویسنده | ||
| Sadegh Eskandari* | ||
| Department of Computer Science, University of Guilan, Rasht, Iran | ||
| چکیده | ||
| Online Streaming Features (OSF) is a data streaming scenario, in which the number of instances is fixed while feature space grows with time. This paper presents a rough sets-based online feature selection algorithm for OSF. The proposed method, which is called OSFS-NRFS, consists of two major steps: (1) online noise resistantly relevance analysis that discards irrelevant features and (2) online noise resistanlty redundancy analysis, which eliminates redundant features. To show the efficiency and accuracy of the proposed algorithm, it is compared with two state-of-the-art rough sets-based OSFS algorithms on eight high-dimensional data sets. The experiments demonstrate that the proposed algorithm is faster and achieves better classification results than the existing methods. | ||
| کلیدواژه‌ها | ||
| Feature Selection؛ Online Feature Selection؛ Streaming Feature Selection؛ Rough Sets | ||
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آمار تعداد مشاهده مقاله: 657 تعداد دریافت فایل اصل مقاله: 646 |
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