Abstract
Nowadays, a large amount of digital data is available due to new technologies and different sources of data such as social networks, sensors, etc. There is a challenge to deal with this high dimensional data because query performance degrades as dimensionality increases. However, most of this data are redundant. Hence, it can be reduced to the smaller number of attributes without significant loss of information. The dimensionality reduction and feature selection techniques can be applied for that. In this paper, we compare two techniques Semi-Discrete Decomposition (SDD) and Singular Value Decomposition (SVD) to select significant features from Hepatitis dataset. We found that SVD is more appropriate than SDD in terms of accuracy and acceptable training time.
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Acknowledgement
This work is partially supported by Grant of SGS No. SP2016/97, VŠB—Technical University of Ostrava, Czech Republic and Arab Open University, Kuwait Branch.
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Hussien, I., Omer, S., Oweis, N.E., Snášel, V. (2016). Feature Selection Using Semi Discrete Decomposition and Singular Value Decompositions. In: Abraham, A., Kovalev, S., Tarassov, V., Snášel, V. (eds) Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16). Advances in Intelligent Systems and Computing, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-319-33609-1_8
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DOI: https://doi.org/10.1007/978-3-319-33609-1_8
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