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CPS2222 Abdullah M.R. et al.
150 86.58 100 13.42 0 0 0.128
15% 20 4.1 100 95.9 0 0 0
40 53.5 100 46.5 0 0 0
100 81.51333 100 18.48667 0 0 0.003
150 85.45778 97.7778 14.54222 2.222 0 0.04866667
20% 20 10.575 100 89.425 0 0 0
40 57.5375 100 42.4625 0 0 0
100 83.73 100 16.27 0 0 0
150 88.60333 100 11.39667 0 0 0.00266667

5. Conclusion
It is crucial to detect outliers in high dimensional data as it may give
misleading conclusion about fitting of regression model. The FP-SVR has been
developed to identify outliers in high dimensional data. Nevertheless, it is not
very successful in detection outliers in high dimensional data. Hence, we
developed Nu-SVR to remedy this problem. The numerical example signify
that Nu-SVR is very successful in detecting high dimensional data in small and
large samples.

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