Mathematical Problems in Engineering / 2015 / Article / Tab 6

Research Article

Sample-Based Extreme Learning Machine with Missing Data

Table 6

Performance comparisons with UCI benchmark data sets. The two rows of each cell represent mean RMSE ± standard deviation over different missing ratios and training time (in seconds).

S-ELM ZF-ELM MF-ELM

Body fat0.1299 ± 0.09050.1781 ± 0.10070.1457 ± 0.1088
0.15600.17580.1872

Housing0.2229 ± 0.10190.3234 ± 0.16970.2511 ± 0.1261
0.56280.34320.3869

Pyrim0.3816 ± 0.08420.5603 ± 0.22530.4537 ± 0.1396
0.06240.10080.0926

Abalone0.2336 ± 0.10830.3004 ± 0.15370.2643 ± 0.1193
213.5177.6182.4

Bike sharing0.3886 ± 0.10200.4662 ± 0.15960.3977 ± 0.1218
7.2637.1096.993

Airfoil self-noise0.3765 ± 0.15910.4563 ± 0.24700.3810 ± 0.1928
52.3850.7451.61

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