OUTLIER DETECTION METHOD BY USING DEEP NEURAL NETWORKS

Year 2017, Volume: 5 Issue: 1, 96 - 101, 30.06.2017

Olgun Aydin Semra Erbolat Tasabat

https://doi.org/10.17261/Pressacademia.2017.577

Abstract

Detecting
outliers in the data set is quite important for building effective predictive
models. Consistent prediction can not be made through models created with data
sets containing outliers, or robust models can not be created. In such cases,
it may be possible to exclude observations that are determined to be outlier
from the data set, or to assign less weight to these points of observation than
to other points of observation. Lower and upper boundaries can be created to
exclude outliers from the dataset, and models can be created using the data
between those boundaries. In this study, it was aimed to propose a different
perspective on outlier detection methods by creating upper bounds with the aid
of deep neural networks using skewness, kurtosis and standard deviation values
obtained from the dataset with trained models.

 

Keywords

Deep neural network, outliers, outlier detection, modelling, predictive modelling

References

• Aggarwal, C.C. (2013), Outlier Analysis, Springer-Verlag New York
• Hawkins, D. (1980), Identification of Outliers Chapman and Hall Hawkins, D. Identification of Outliers.
• Chapman and Hall. http://www.cse.yorku.ca/~jarek/courses/6412/lectures/Outliers.ppt http://deeplearning.net/tutorial/
• http://www.iro.umontreal.ca/~pift6266/H10/notes/deepintro.html
• Ben-Gal, Irad. "Outlier detection." Data mining and knowledge discovery handbook (2005): 131-146.
• Osborne, Jason W., and Amy Overbay. "The power of outliers (and why researchers should always check for them)." Practical assessment, research & evaluation 9.6 (2004): 1-12.