SFtools-package: SFtools: Space Filling Based Tools for Data Mining

In mlaib/SFtools: Space Filling Based Tools for Data Mining

Description

Contains space filling based tools for machine learning and data mining. Some functions offer several computational techniques and deal with the out of memory for large big data by using the ff package.

Author(s)

Mohamed Laib [e-mail] and

Mikhail Kanevski [e-mail],

Maintainer: Mohamed Laib [e-mail]

References

M. Laib, M. Kanevski, A novel filter algorithm for unsupervised feature selection based on a space filling measure. Proceedings of the 26rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp. 485-490, Bruges (Belgium), 2018.

M. Laib and M. Kanevski, A new algorithm for redundancy minimisation in geo-environmental data, 2019. Computers & Geosciences, 133 104328.

J. A. Royle, D. Nychka, An algorithm for the construction of spatial coverage designs with implementation in Splus, Computers and Geosciences 24 (1997) p. 479–488.

J. Franco, Planification d’expériences numériques en phase exploratoire pour la simulation des phénomènes complexes, Thesis (2008) 282.

D. Dupuy, C. Helbert, J. Franco (2015). DiceDesign and DiceEval: Two R Packages for Design and Analysis of Computer Experiments. Journal of Statistical Software, 65(11), 1-38. Jstatsoft.

See Also

Useful links:

• https://mlaib.github.io/

• Report bugs at https://github.com/mlaib/SFtools/issues

mlaib/SFtools documentation built on Feb. 1, 2021, 6:11 p.m.