Skip to main content

Table 2 Cross-validated, TT-corrected performances of SES and LASSO-type methods on the four scenarios

From: Feature selection for high-dimensional temporal data

Temporal-longitudinal scenario

Temporal-distinct scenario

 

MSPE

Selected vars

 

MSPE

Selected vars

Dataset

SESglmm

glmmLasso

SESglmm

glmmLasso

Dataset

SES

LASSO

SES

LASSO

GDS5088

0.081 (0.026)

0.160 (0.042)

5.25 (0.85)

5.15 (8.65)

GDS3859

0.068 (0.006)

0.019 (0.002)

3.5 (0.51)

11.81 (4.66)

GDS4395

0.104 (0.041)

0.640 (0.568)

5.37 (0.56)

12.35 (13.61)

GDS972

0.022 (0.000)

0.001 (0.000)

5.83 (0.92)

22.2 (9.85)

GDS4822

0.115 (0.484)

0.765 (0.436)

4.75 (0.85)

3.16 (5.77)

GDS947

0.056 (0.000)

0.054 (0.026)

5.92 (0.65)

12.40 (5.40)

GDS3326

0.135 (0.021)

0.234 (0.139)

5.42 (0.78)

2.42 (7.45)

GDS964

0.033 (0.000)

0.003 (0.000)

5.73 (0.69)

25.69 (11.86)

GDS3181

0.971 (0.484)

0.684 (0.257)

4.17 (0.87)

0.35 (2.15)

GDS2688

0.184 (0.006)

0.005 (0.001)

5.79 (1.06)

20.64 (10.93)

GDS4258

0.234 (0.096)

9.882 (4.518)

3.83 (0.51)

1.48 (4.06)

GDS2135

0.053 (0.002)

0.014 (0.003)

3.80 (0.76)

10 (5.72)

GDS3432

0.357 (0.017)

2.283 (1.572)

1.67 (3.51)

0.08 (0.55)

Av. diff.

0.053 a

-12.03 a

GDS3915

0.059 (0.002)

0.150 (0.055)

5.12 (0.80)

1.66 (4.62)

 

Av. diff.

-1.59 b

1.12 b

 

Static-distinct scenario

Static-longitudinal scenario

 

PCC

Selected vars

 

PCC

Selected vars

Dataset

SES

LASSO

SES

LASSO

Dataset

SES

GLASSO

SES

GLASSO

GDS4319

0.873 (0.000)

0.995 (0.000)

2.1 (0.31)

8 (0.00)

GDS4146

1.000 (0.000)

0.858 (0.142)

1.00 (0.00)

0.42 (1.38)

GDS3924

0.729 (0.000)

0.528 (0.104)

2.75 (0.44)

53.56 (28.55)

GDS4518

0.750 (0.000)

0.417 (0.333)

1.75 (0.44)

3.04 (2.15)

GDS3184

0.556 (0.067)

0.578 (0.111)

3.00 (0.00)

10.62 (5.16)

GDS4820

0.500 (0.000)

0.667 (0.167)

2.00 (0.00)

5.14 (3.19)

GDS3145

0.953 (0.000)

0.594 (0.125)

1.5 (0.88)

0.6 (0.55)

GDS1840

0.625 (0.000)

0.500 (0.250)

1.5 (0.51)

2.67 (2.03)

GDS2882

0.800 (0.000)

0.750 (0.000)

1.5 (0.88)

0.25 (0.50)

Av. diff.

0.108

-1.23

GDS2851

0.722 (0.000)

0.694 (0.000)

2.25 (0.44)

0.75 (0.50)

 

GDS1784

0.861 (0.000)

0.694 (0.000)

1.75 (0.85)

0.5 (0.58)

 

GDS2456

1.000 (0.000)

0.739 (0.000)

1.2 (0.41)

0.44 (0.53)

 

Av. diff.

0.115 b

-6.52 b

 
  1. For each dataset, performances are reported as average (st.d.). Zero standard deviations are caused by numerical rounding. For Temporal-longitudinal and Temporal-distinct scenario’s performance are computed as Mean Squared Prediction Error (MSPE, lower values indicate better performances) and number of selected variables, while for the other scenarios the Percentage of Corrected Classification (PCC, the higher the better) is used instead of MSPE. The bold numbers indicate better performance; average differences over all datasets are reported for each scenario. Symbols a and b denote average differences that are statistically significant at 0.01 and 0.05, respectively. In terms of predictive performances, SES is always on par or better than LASSO type algorithms in all scenarios except for the Temporal-distinct
Back to article page

BMC Bioinformatics

ISSN: 1471-2105

Contact us