1. Find the Features of Noses
Jia Wu
11/25/2008

2. Nasal Alae Local minimal Large slope
Nasal Alae feature= 2*width of nasal ala/ width of nose
Nostril
lateral view

4. Find bulbous nasal tip
π/4 = 0.785
0.5

5. F14_9_1_1
F14_0_0_1

6. 0.51 0.38 0.61 0.41 Average value for labeled as bulbous tip
19/20 = 0.95
9/20 = 0.45
19/Depth of nose
19/Depth of nose
Average value for labeled as bulbous tip
Average value for those without bulbous tip label
0.51
0.38
Average value for labeled as bulbous tip(new)
Average value for those without bulbous tip label
0.61
0.41

7. Proportion of upper part Proportion of lower part
π/4 = 0.785
1/4 = 0.25
3/4 = 0.75

8. Compare with triangle
F14_0_0_1
F14_9_1_1
Overlap of some information

9. Bulbous nasal tip(Top 10)
Data
Carrie
Anne
Mark
Predicted
severeness
F1_11_0_6 2 1
0.8
M7_0_0_1
NoData
0.69
F3_0_0_1
0.90
0.70
M5_0_0_1
0.84
F18_0_0_1
0.83
F10_0_0_2
0.78
0.71
F4_0_0_2
0.76
0.75
F31_0_0_1
0.74
F13_0_0_1
0.72
0.89
0.5

10. Bulbous nasal tip(Top 10,ranked by severeness)
Data
Carrie
Anne
Mark
severeness
Predicted
F13_0_0_1 2 1
0.89
0.72
M5_0_0_1
NoData
0.84
F18_0_0_1
0.83
F1_11_0_6
0.8
F4_0_0_2
0.75
0.76
F31_0_0_1
0.74
F3_0_0_1
0.70
0.90
F10_0_0_2
0.71
0.78
M7_0_0_1
0.69
0.5
coincidence

11. Features
1. height of nose/height of face (Deleted) ----> Depth of nose/depth of face
2. width of nose/width of face
3. width of nose/height of nose
4. width of top nose /width of bottom nose
5. angle top-side-side
6. width of top nose/ width of face
7. bulbous information
8. bulbous triangle compare infomation
9. bulbous upper part information
10. angle bottom-tip-top
11. angle side-tip-side
12. width of nasal alae/ width of nose
13. width of nasal alae/ width of face

12. W86, NaiveBayes Correctly Classified Instances 69 80.2326 %
Incorrectly Classified Instances %
Kappa statistic
Mean absolute error
Root mean squared error
Relative absolute error %
Root relative squared error %
Total Number of Instances
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure Class
=== Confusion Matrix ===
a b <-- classified as
35 8 | a = 0(Affected)
9 34 | b = 1(Control)

13. Selected attributes number of folds (%) attribute 0( 0 %) 1 sex
0( 0 %) 2 year
0( 0 %) 3 Tp
0( 0 %) 4 Wp
0( 0 %) 5 WH
0( 0 %) 6 WTNWN
4( 40 %) 7 AngleTopSide (Tubular looking)
0( 0 %) 8 WTNp
4( 40 %) 9 Bulbous1
10(100 %) 10 Bulbous2
10(100 %) 11 BulbousUp
0( 0 %) 12 ABottomTipTop
0( 0 %) 13 ALeftTipRight
1( 10 %) 14 Pinched1 (width of nasal alae/ width of nose)
0( 0 %) 15 Pinched2

14. Bulbous Nasal Tip bulbous severeness Compare with triangle
bulbous upper part
Unaffected
Affected

15. Angle top-side-side
tubular appearance
Unaffected
Affected

16. Nasal Alae
Unaffected
Affected

17. Analysing: F_measure Datasets: 8 Resultsets: 4
Confidence: 0.05 (two tailed)
Date:
Dataset (1) W86_25D_ | (2) W86_3 (3) W86_n (4) W86_f
rules.ZeroR (100) |
rules.NNge (100) | *
rules.JRip (100) | *
trees.J (100) | *
lazy.IB (100) | v
lazy.IBk (100) | v
functions.SMO (400) | v
bayes.NaiveBayes (100) | *
(v/ /*) | (1/7/0) (1/3/4) (1/7/0)
Skipped:
Key:
(1) W86_25D_pca
(2) W86_3Dsnp_cut_pca
(3) W86_noseRect_pca
(4) W86_features3_4-unsupervised.attribute.Remove-R3-4

18. Analysing: IR_precision
Datasets: 8
Resultsets: 4
Confidence: 0.05 (two tailed)
Date:
Dataset (1) W86_25D_ | (2) W86_3 (3) W86_n (4) W86_f
rules.ZeroR (100) |
rules.NNge (100) | *
rules.JRip (100) | *
trees.J (100) | *
lazy.IB (100) |
lazy.IBk (100) |
functions.SMO (400) |
bayes.NaiveBayes (100) | *
(v/ /*) | (0/8/0) (0/4/4) (0/8/0)
Skipped:
Key:
(1) W86_25D_pca
(2) W86_3Dsnp_cut_pca
(3) W86_noseRect_pca
(4) W86_features3_4-unsupervised.attribute.Remove-R3-4

19. Analysing: IR_recall Datasets: 8 Resultsets: 4
Confidence: 0.05 (two tailed)
Date:
Dataset (1) W86_25D_ | (2) W86_3 (3) W86_n (4) W86_f
rules.ZeroR (100) |
rules.NNge (100) |
rules.JRip (100) |
trees.J (100) | *
lazy.IB (100) | v v
lazy.IBk (100) | v v v
functions.SMO (400) | v
bayes.NaiveBayes (100) | *
(v/ /*) | (1/7/0) (2/4/2) (3/5/0)
Skipped:
Key:
(1) W86_25D_pca
(2) W86_3Dsnp_cut_pca
(3) W86_noseRect_pca
(4) W86_features3_4-unsupervised.attribute.Remove-R3-4

20. Analysing: IR_precision
Datasets: 8
Resultsets: 4
Confidence: 0.05 (two tailed)
Date:
Dataset (1) ALL_25D_ | (2) ALL_3 (3) ALL_n (4) InfoF
rules.ZeroR (100) |
rules.NNge (100) | *
rules.JRip (100) | *
trees.J (100) | *
lazy.IB (100) | v
lazy.IBk (100) |
functions.SMO (100) |
bayes.NaiveBayes (100) | *
(v/ /*) | (0/8/0) (0/4/4) (1/7/0)
Skipped:
Key:
(1) ALL_25D_pca
(2) ALL_3Dsnp_pca
(3) ALL_noseRect_pca
(4) InfoFmeasures3_4-unsupervised.attribute.Remove-R3-4

21. Analysing: IR_recall
Datasets: 8
Resultsets: 4
Confidence: 0.05 (two tailed)
Date:
Dataset (1) ALL_25D_ | (2) ALL_3 (3) ALL_n (4) InfoF
rules.ZeroR (100) |
rules.NNge (100) | * v
rules.JRip (100) | *
trees.J (100) |
lazy.IB (100) | v v
lazy.IBk (100) | v v v
functions.SMO (100) | v
bayes.NaiveBayes (100) | * *
(v/ /*) | (1/6/1) (2/3/3) (4/4/0)
Skipped:
Key:
(1) ALL_25D_pca
(2) ALL_3Dsnp_pca
(3) ALL_noseRect_pca
(4) InfoFmeasures3_4-unsupervised.attribute.Remove-R3-4

22. Analysing: F_measure
Datasets: 8
Resultsets: 4
Confidence: 0.05 (two tailed)
Date:
Dataset (1) ALL_25D_ | (2) ALL_3 (3) ALL_n (4) InfoF
rules.ZeroR (100) |
rules.NNge (100) | * v
rules.JRip (100) | *
trees.J (100) | *
lazy.IB (100) | v v
lazy.IBk (100) | v v
functions.SMO (100) | v
bayes.NaiveBayes (100) | *
(v/ /*) | (1/7/0) (1/3/4) (4/4/0)
Skipped:
Key:
(1) ALL_25D_pca
(2) ALL_3Dsnp_pca
(3) ALL_noseRect_pca
(4) InfoFmeasures3_4-unsupervised.attribute.Remove-R3-4