Outlier detection-Reference-Cited by-同舟云学术

Outlier detection

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing - AIIPCC '19
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Author:

Yang Jiawei¹,Rahardja Susanto²,Fränti Pasi¹

Affiliation:

1. University of Eastern Finland, Joensuu, Finland

2. Northwestern Polytechnical University, Xi'an, Shaanxi, China

Publisher

ACM Press

Reference16 articles.

1. C Leys, C Ley, O Klein, P Bernard and L Licata (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. J Exp Soc Psychol, 49(4), 764--766.

2. M Gupta, J Gao, C Aggawal and J Han (2014). Outlier Detection for Temporal Data. Morgan & Claypool Publishers.

3. T V Pollet and L van der Meij (2017). To remove or not to remove: the impact of outlier handling on significance testing in testosterone data. Adaptive Human Behavior and Physiology, 3(1), 43--60.

4. J P Simmons, L D Nelson and U Simonsohn (2011). False positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359--1366.

5. J W Yang, S Rahardja and P Fränti (2018). Mean-shift outlier detection. Int. Conf. Fuzzy Systems and Data Mining (FSDM), In Frontiers in Artificial Intelligence and Applications (FAIA), 309(2), 208--215.

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