نمونه برداری هوشمند و یادگیری تابع افزایشی
Abstract
Keywords
1- Introduction
2- Data sampling methods
3- Comparison of sampling methods
4- Smart sampling and incremental function learning algorithm
5- Results
6- Conclusions
Acknowledgments
References
Abstract
Very large high dimensional data are common nowadays and they impose new challenges to data-driven and data-intensive algorithms. Computational Intelligence techniques have the potential to provide powerful tools for addressing these challenges, but the current literature focuses mainly on handling scalability issues related to data volume in terms of sample size for classification tasks.
This work presents a systematic and comprehensive approach for optimally handling regression tasks with very large high dimensional data. The proposed approach is based on smart sampling techniques for minimizing the number of samples to be generated by using an iterative approach that creates new sample sets until the input and output space of the function to be approximated are optimally covered. Incremental function learning takes place in each sampling iteration, the new samples are used to fine tune the regression results of the function learning algorithm. The accuracy and confidence levels of the resulting approximation function are assessed using the probably approximately correct computation framework.
The smart sampling and incremental function learning techniques can be easily used in practical applications and scale well in the case of extremely large data. The feasibility and good results of the proposed techniques are demonstrated using benchmark functions as well as functions from real-world problems.
Introduction
Computer-based simulations of tremendously complex mathematical systems describing multifaceted physical, chemical, dynamical and engineering models are usually associated with very expensive costs in terms of processing time and storage. Complex mathematical models are present in a wide variety of scientific areas such as the simulation of atmospheric processes in numerical weather prediction (Han & Pan, 2011; Hsieh & Tang, 1998; Lynch, 2006; Morcrette, 1991), climate modeling (Flato et al., 2013), (Gordon et al., 2000), chemical transport (Grell et al., 2005), (Menut et al., 2013), radiative transfer (Gimeno García, Trautmann, & Venema, 2012) and large eddy simulations (Sagaut, 2006). Other scientific disciplines such as genetics, aerodynamics, or statistical mechanics also make use of highly complex models. The input space of these models can be of high dimensionality with hundreds or more components. The usage of more realistic models usually introduces new dimensions leading to an exponential increase in volume, i.e. ‘‘Big Data’’ (Hilbert & López, 2011; Lynch, 2008).