The arboretum procedure



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documentation -> From cyber-crime to insider trading, digital investigators are increasingly being asked to
documentation -> EnCase Forensic Transform Your Investigations
documentation -> File Sharing Documentation Prepared by Alan Halter Created: 1/7/2016 Modified: 1/7/2016
documentation -> Gaia Data Release 1 Documentation release 0

8

§

PROC DMNEURL: Approximation to PROC NEURAL



COS: Iter=3 Crit=0.06277476: SSE=741.349891 Acc= 83.2886

------------ Activation= EXP (Stage=2) -----------------

NOTE: ABSGCONV convergence criterion satisfied.

EXP: Iter=4 Crit=0.06131052: SSE=721.679089 Acc= 83.4396

The best accuracy increased from 83.29 to 83.47 and the (1,1) entry from 319 to 343

counts:

Classification Table for CUTOFF = 0.5000

Predicted

Activation

Accuracy

Observed

1

0

SQUARE

83.473154

1

343.0

846.0

721.118524

0

139.0

4632.0

EXP

83.422819

1

337.0

852.0

721.301910

0

136.0

4635.0

LOGIST

83.607383

1

337.0

852.0

724.687746

0

125.0

4646.0

TANH

83.607383

1

340.0

849.0

725.553808

0

128.0

4643.0

ARCTAN

83.607383

1

341.0

848.0

725.724668

0

129.0

4642.0

SIN

83.607383

1

340.0

849.0

725.889780

0

128.0

4643.0

GAUSS

83.372483

1

317.0

872.0

741.471407

0

119.0

4652.0

COS

83.322148

1

316.0

873.0

741.668156

0

121.0

4650.0

Even though SQUARE shows the best SSE, the accuracy rates for some other func-

tions (e.g. LOGIST) are slightly better:

Goodness-of-Fit Criteria (Ordered by SSE, Stage 2)

Run

Activation

SSE

RMSE

Accuracy

1

SQUARE

721.11852

0.347841

83.473154

8

EXP

721.30191

0.347885

83.422819

4

LOGIST

724.68775

0.348700

83.607383

2

TANH

725.55381

0.348909

83.607383

3

ARCTAN

725.72467

0.348950

83.607383

6

SIN

725.88978

0.348989

83.607383

5

GAUSS

741.47141

0.352715

83.372483

7

COS

741.66816

0.352762

83.322148

Component selection w.r.t. the residuals of the stage 2 starts the estimation of stage

3. Note, that the

Ê

¾



values become smaller and smaller.


Purpose of PROC DMNEURL

§

9



Component Selection: SS(y) and R2 (Stage=3)

Comp

Eigval

R-Square

F Value

p-Value

8

6938.083228

0.005571

33.383374

<.0001

20

5345.603436

0.004223

25.409312

<.0001

12

6136.575271

0.004059

24.517995

<.0001

Also the size of the objective function at the optimization results decreases:



------------ Optimization Cycle (Stage=3) --------------

------------ Activation= SQUARE (Stage=3) --------------

NOTE: ABSGCONV convergence criterion satisfied.

SQUARE: Iter=1 Crit=0.06049339: SSE=710.516275 Acc= 83.7081

------------ Activation= TANH (Stage=3) ----------------

NOTE: ABSGCONV convergence criterion satisfied.

TANH: Iter=4 Crit=0.06052425: SSE=710.396136 Acc= 83.7752

------------ Activation= ARCTAN (Stage=3) --------------

NOTE: ABSGCONV convergence criterion satisfied.

ARCTAN: Iter=3 Crit=0.06052607: SSE=710.489715 Acc= 83.7081

------------ Activation= LOGIST (Stage=3) --------------

NOTE: ABSGCONV convergence criterion satisfied.

LOGIST: Iter=6 Crit=0.06055936: SSE=711.054572 Acc= 83.6577

------------ Activation= GAUSS (Stage=3) ---------------

NOTE: ABSGCONV convergence criterion satisfied.

GAUSS: Iter=6 Crit=0.06111674: SSE= 719.41694 Acc= 83.3725

------------ Activation= SIN (Stage=3) -----------------

NOTE: ABSGCONV convergence criterion satisfied.

SIN: Iter=3 Crit=0.06051959: SSE=710.308709 Acc= 83.8087

------------ Activation= COS (Stage=3) -----------------

NOTE: ABSGCONV convergence criterion satisfied.

COS: Iter=6 Crit=0.06117044: SSE=719.262211 Acc= 83.3725

------------ Activation= EXP (Stage=3) -----------------

NOTE: ABSGCONV convergence criterion satisfied.

EXP: Iter=2 Crit=0.06051088: SSE=710.810558 Acc= 83.7081

The accuracy of the best fit impoves slightly from 83.47 to 83.79 and the size of the

(1,1) entry inceases from 343 to 364.

Classification Table for CUTOFF = 0.5000

Predicted

Activation

Accuracy

Observed

1

0

SIN

83.791946

1

364.0

825.0

709.778632

0

141.0

4630.0

TANH

83.758389

1

363.0

826.0




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