Medical data mining in sentiment analysis based on optimized swarm search feature selection-Reference-Cited by-同舟云学术

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Medical data mining in sentiment analysis based on optimized swarm search feature selection

Published:2018-09-11 Issue:4 Volume:41 Page:1087-1100
ISSN:0158-9938
Container-title:Australasian Physical & Engineering Sciences in Medicine
language:en
Short-container-title:Australas Phys Eng Sci Med

Author:

Zeng Daohui,Peng Jidong,Fong Simon,Qiu Yining,Wong Raymond

Funder

Temporal Data Stream Mining by Using Incrementally Optimized Very Fast Decision Forest

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,Biomedical Engineering,Biophysics,General Physics and Astronomy

Link

http://link.springer.com/article/10.1007/s13246-018-0674-3/fulltext.html

Reference23 articles.

1. Lakshminarayan CK (2013) High dimensional big data and pattern analysis: a tutorial. In: Bhatnagar V, Srinivasa S (eds) Big data analytics, Lecture Notes in Computer Science, Springer, Cham. https://doi.org/10.1007/978-3-319-03689-2_5

2. Yusta SC (2009) Different metaheuristic strategies to solve the feature selection problem. Pattern Recognit Lett 30(5):525–534. https://doi.org/10.1016/j.patrec.2008.11.012

3. Fong S, Deb S, Yang XS, Li J (2014) Feature selection in life science classification: metaheuristic swarm search. IEEE IT Prof 16(4):24–29. https://doi.org/10.1109/MITP.2014.50

4. Tsamardinos I, Aliferis CF, Statnikov A (2003) Time and sample efficient discovery of markov blankets and direct causal relations. In Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining, ACM Press, pp. 673–678

5. Song Q, Ni J, Wang G (2013) A fast clustering-based feature subset selection algorithm for high-dimensional data, IEEE Trans Knowl Data Eng 25(1):1–14. https://doi.org/10.1109/TKDE.2011.181

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