This slideshow is about the time series classifier algorithm, ShiftTree. ShiftTree is a unique, model-based approach for time series classification. The basic idea is that we assign a cursor (or eye) to each series and move this to certain positions on the time axis. We generate dynamic attributes by answering two questions: (1) Where to look? (2) What to look at?. The answer to the first question tells us where to move the cursor (e.g.: forward 100 steps, to the previous local maxima, etc), while the second answer defines the calculation of the dynamic attributes (e.g.: value at that point, the weighted avarage of the values around the position, the difference in the current and previous cursor position, etc). These dynamic attributes then used in a binary decision tree. This slideshow was originally presented at ECML/PKDD 2011 in Athens. Note that for whatever reasons SlideShare doesn't support animations. Therefore the animated slides were split into multiple slides.

1. SHIFTTREE:
AN INTERPRETABLE MODEL-BASED APPROACH FOR
TIME SERIES CLASSIFICATION
Balázs Hidasi - hidasi@tmit.bme.hu
Csaba Gáspár-Papanek - gaspar@tmit.bme.hu
Budapest University of Technology and Economics,
Hungary
ECML/PKDD, 6TH SEPTEMBER 2011, ATHENS

2. OUTLINE
 General concept
 Task & adaptation
 Labeling & learning
 Evaluation
 Forest methods
 Conclusion
2/17

3. GENERAL CONCEPT
 Not limited to time series
 Time series
 Semi-structured data
 Graphs
 Two questions:
 „Where to look at?”
 „What to observe?”
 Operator families
 EyeShifter Operator(s) (ESO)
F(x)
 ConditionBuilder Operators (CBO)
 Dynamic attributes
 Model: sequence of simple operatos / rules 3/17

4. TIME SERIES CLASSIFICATION
 Time series Learning
Model
 With one observed variable
 Evenly sampled
 The algorithm has no such limitations
 Standard classification task
 Focusing on accuracy Model
Labeling
 Goals
 Satisfying accuracy
 Model-based instead of memory-based
4/17
 Interpretable models

5. EXPLAINING THE GOALS
 Why model-based?
 Labeling is much faster 
 Can learn more general properties of the classes 
 Needs more training samples 
 What is interpretability good for?
 Getting user trust 
 Helping in understanding the data 
5/17

6. CONCEPT APPLIED TO TIME SERIES
 Dynamic attributes of time series
 EyeShifter Operator (ESO): „Where to look at?”
 Moves a cursor along the time axis
 To a specified point of the series
 E.g.: „To the next local minimum”, „100 steps ahead”, etc.
 Note: result of operator sequence depens on the order
 ConditionBuilder Operator (CBO): „What to observe?”
F(x)
 Computes the dynamic attribute
 E.g.: „Value at the given time”, „Length of the jump”, etc.
 Decision tree as model Weighted average
 Works well with the dynamic attribute concept 6/17
 Operator sequence from root to leaf

7. LABELING
Level 0
ESO: To global max
CBO: Value at time
Threshold: 1,20775
Level 1 Level 1
ESO: Step forward 100 ESO: To next local max
CBO: Value at time CBO: Value at time
Threshold : 0,390093 Threshold : -0,700739
Level 2 Level 2 Level 2 Level 2
7/17
Leaf Leaf Leaf Leaf

8. LABELING
Level 0
ESO: To global max
CBO: Value at time
Threshold: 1,20775
Level 1 Level 1
ESO: Step forward 100 ESO: To next local max
CBO: Value at time CBO: Value at time
Threshold : 0,390093 Threshold : -0,700739
Level 2 Level 2 Level 2 Level 2
7/17
Leaf Leaf Leaf Leaf

9. LABELING
Dynamic attribute value: 0,836848
Level 0
ESO: To global max
CBO: Value at time
Threshold: 1,20775
Level 1 Level 1
ESO: Step forward 100 ESO: To next local max
CBO: Value at time CBO: Value at time
Threshold : 0,390093 Threshold : -0,700739
Level 2 Level 2 Level 2 Level 2
7/17
Leaf Leaf Leaf Leaf

10. LABELING
Dynamic attribute value: 0,836848
Level 0
ESO: To global max
CBO: Value at time
Threshold: 1,20775
Level 1 Level 1
ESO: Step forward 100 ESO: To next local max
CBO: Value at time CBO: Value at time
Threshold : 0,390093 Threshold : -0,700739
Level 2 Level 2 Level 2 Level 2
7/17
Leaf Leaf Leaf Leaf

11. LABELING
Level 0
ESO: To global max
CBO: Value at time
Threshold: 1,20775
Level 1 Level 1
ESO: Step forward 100 ESO: To next local max
CBO: Value at time CBO: Value at time
Threshold : 0,390093 Threshold : -0,700739
Level 2 Level 2 Level 2 Level 2
7/17
Leaf Leaf Leaf Leaf

12. LABELING
Level 0
ESO: To global max
CBO: Value at time
Threshold: 1,20775
Level 1 Level 1
ESO: Step forward 100 ESO: To next local max
CBO: Value at time CBO: Value at time
Threshold : 0,390093 Threshold : -0,700739
Level 2 Level 2 Level 2 Level 2
7/17
Leaf Leaf Leaf Leaf

13. LABELING
Dynamic attribute value: 0,767139
Level 0
ESO: To global max
CBO: Value at time
Threshold: 1,20775
Level 1 Level 1
ESO: Step forward 100 ESO: To next local max
CBO: Value at time CBO: Value at time
Threshold : 0,390093 Threshold : -0,700739
Level 2 Level 2 Level 2 Level 2
7/17
Leaf Leaf Leaf Leaf

14. LABELING
Dynamic attribute value: 0,767139
Level 0
ESO: To global max
CBO: Value at time
Threshold: 1,20775
Level 1 Level 1
ESO: Step forward 100 ESO: To next local max
CBO: Value at time CBO: Value at time
Threshold : 0,390093 Threshold : -0,700739
Level 2 Level 2 Level 2 Level 2
7/17
Leaf Leaf Leaf Leaf

15. LABELING
Level 0
ESO: To global max
CBO: Value at time
Threshold: 1,20775
Level 1 Level 1
ESO: Step forward 100 ESO: To next local max
CBO: Value at time CBO: Value at time
Threshold : 0,390093 Threshold : -0,700739
Level 2 Level 2 Level 2 Level 2
7/17
Leaf Leaf Leaf Leaf

16. LEARNING (1/3)
 Operator pool definition: ESOs, CBOs
 Root node
 Cursor starts at the beginning
 May be overridden
8/17

17. F(x)
LEARNING (2/3)
 Available dynamic attributes (CBO pool)
 At/around reachable positions
 Selecting a…
 Attribute
 Threshold value
Minimize weighted child node entropy
9/17

18. LEARNING (3/3)
 Best split selected
 Cursor is moved by ESO
 Reachable positions will change
 Train set split
 Child nodes continue the learning
process
 Stop: homogenous nodes
10/17

19. INTERPRETABILITY
 Depends on the operators
 Helps undepstanding the data
 E.g.: CBF dataset
 Z-normalized, 3 class: Cylinder, Bell, Funnel
 Distinguish cylinder from bell and funnel by global maxima
 The data is z-normalized (standardized)
 Distinguish bell from funnel by stepping back 25 steps + noise 11/17
filtering through weighted average
 On which side is the peak

20. PERFORMANCE OF THE ALGORITHM
 Databases
 UCR: mostly smaller data sets (20)
 Ford: larger data sets (2)
 No optimizations in the experiments
 Performance
 Better on larger data sets (model-based)
100.00% 94.97% 96.52%
92.94%
90.00%
81.29% 83.19%
80.00% 74.67% 73.33%
69.11%
70.00% 64.93%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
12/17
0.00%
Smaller UCR sets (15) Larger UCR sets (5) Ford (largest) sets (2)
1NN Eucledian 1NN DTW ShiftTree

21. ADVANTAGES AND DRAWBACKS
 Advantages
 Advantages of being model based (fast labeling, etc)
 Interpretable (depens on operators)
 Can be used independently of application domain
 Expert’s knowledge can be built in through operators
 Disadvantages
 Optimization of the operators is not trivial
 Learning may take a long time when too many
operators used
 Disadvantages of the model based methods (larger
training set required, etc) 13/17

22. SHIFTFOREST: COMBINING MULTIPLE MODELS
 Combining models enhances accuracy
 Boosting
 Some issues on smaller datasets
 The „XV” method
 Splitting training set (random splits)
 Building model on the first part, evaluating on the
second
 Model weight: the measured accuracy
14/17

23. SHIFTFOREST RESULTS
 Accuracy increases
 Interpretability is often lost
 May the intersection of the trees can be interpreted
100.00% 96.52%97.77%
92.94%94.97%
88.87%
90.00% 85.93%
81.29% 83.19%
80.00% 74.67% 73.33%
69.11%
70.00% 64.93%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
Smaller UCR sets (15) Larger UCR sets (5) Ford (largest) sets (2)
15/17
1NN Eucledian 1NN DTW ShiftTree ShiftForest

24. CONCLUSION
 Novel model-based algorithm
 Uses cursors and operators
 Cursor movement
 Attribute computation
 Sequence of simple operators as the model
 Decision tree
 Base of a whole algorithm family
16/17

25. THANK YOU FOR YOUR ATTENTION!
Balázs Hidasi Csaba Gáspár-Papanek
(hidasi@tmit.bme.hu) (gaspar@tmit.bme.hu)
17/17
For more ShiftTree related research visit my side: http://www.hidasi.eu

26. TIME OF MODEL BUILDING, SCALABILITY
 O(N*log(N)) per node (ordering)
 Time of model building
 Depends on tree structure
200
 Acceptable
180
160
140
 Scales linearly 120
Time (s)
 Experiments 100
80
60
40
20
0
Size of training set
Min of 20 Max of 20 Mean of 20

27. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
ESOMax
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,332858
-0,700739
0,390093
1,02775
- Best threshold by current dynamic attribute: -0,332858
-0,739969
-0,700739
0,390093
0,369887
-1,16155
1,02775
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,985078
0,983634
0,600131
1,32624
Inf
0
Best score in the node (so far): 0,985078
0,983634
0,805777
Inf
0
Best ordering: 27

28. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
ESOMax
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,332858
-0,700739
0,390093
1,02775
- Best threshold by current dynamic attribute: -0,332858
-0,739969
-0,700739
0,390093
0,369887
-1,16155
1,02775
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,985078
0,983634
0,600131
1,32624
Inf
0
Best score in the node (so far): 0,985078
0,983634
0,805777
Inf
0
Best ordering: 28

29. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
ESOMax
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,332858
-0,700739
0,390093
1,02775
- Best threshold by current dynamic attribute: -0,332858
-0,739969
-0,700739
0,390093
0,369887
-1,16155
1,02775
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,985078
0,983634
0,600131
1,32624
Inf
0
Best score in the node (so far): 0,985078
0,983634
0,805777
Inf
0
Best ordering: 29

30. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
ESOMax
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,332858
-0,700739
0,390093
1,02775
- Best threshold by current dynamic attribute: -0,332858
-0,739969
-0,700739
0,390093
0,369887
-1,16155
1,02775
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,985078
0,983634
0,600131
1,32624
Inf
0
Best score in the node (so far): 0,985078
0,983634
0,805777
Inf
0
Best ordering: 30

31. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
ESOMax
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,332858
-0,700739
0,390093
1,02775
- Best threshold by current dynamic attribute: -0,332858
-0,739969
-0,700739
0,390093
0,369887
-1,16155
1,02775
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,985078
0,983634
0,600131
1,32624
Inf
0
Best score in the node (so far): 0,985078
0,983634
0,805777
Inf
0
Best ordering: 31

32. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
ESOMax
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,332858
-0,700739
0,390093
1,02775
- Best threshold by current dynamic attribute: -0,332858
-0,739969
-0,700739
0,390093
0,369887
-1,16155
1,02775
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,985078
0,983634
0,600131
1,32624
Inf
0
Best score in the node (so far): 0,985078
0,983634
0,805777
Inf
0
Best ordering: 32

33. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
ESOMax
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,332858
-0,700739
0,390093
1,02775
- Best threshold by current dynamic attribute: -0,332858
-0,739969
-0,700739
0,390093
0,369887
-1,16155
1,02775
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,985078
0,983634
0,600131
1,32624
Inf
0
Best score in the node (so far): 0,985078
0,983634
0,805777
Inf
0
Best ordering: 33

34. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
ESOMax
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,332858
-0,700739
0,390093
1,02775
- Best threshold by current dynamic attribute: -0,739969
-0,700739
0,390093
0,369887
-1,16155
1,02775
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,983634
0,600131
1,32624
Inf
0
Best score in the node (so far): 0,985078
0,983634
0,805777
Inf
0
Best ordering: 34

35. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
ESOMax
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,332858
-0,700739
0,390093
1,02775
- Best threshold by current dynamic attribute: -0,739969
-0,700739
0,390093
0,369887
-1,16155
1,02775
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,983634
0,600131
1,32624
Inf
0
Best score in the node (so far): 0,985078
0,983634
0,805777
Inf
0
Best ordering: 35

36. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
ESOMax
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,332858
-0,700739
0,390093
1,02775
- Best threshold by current dynamic attribute: -0,739969
-0,700739
0,390093
0,369887
-1,16155
1,02775
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,983634
0,600131
1,32624
Inf
0
Best score in the node (so far): 0,985078
0,983634
0,805777
Inf
0
Best ordering: 36

37. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
ESOMax
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,332858
-0,700739
0,390093
1,02775
- Best threshold by current dynamic attribute: -0,739969
-0,700739
0,390093
0,369887
1,02775
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,983634
0,600131
Inf
0
Best score in the node (so far): 0,983634
0,805777
Inf
0
Best ordering: 37

38. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
ESOMax
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,700739
0,390093
1,02775
- Best threshold by current dynamic attribute: -0,739969
-0,700739
0,390093
0,369887
1,02775
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,983634
0,600131
Inf
0
Best score in the node (so far): 0,983634
0,805777
Inf
0
Best ordering: 38

39. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,700739
0,390093
- Best threshold by current dynamic attribute: -0,739969
-0,700739
0,390093
0,369887
1,02775
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,983634
0,600131
Inf
0
Best score in the node (so far): 0,983634
0,805777
Inf
0
Best ordering: 39

40. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,700739
0,390093
- Best threshold by current dynamic attribute: -0,739969
-0,700739
0,390093
0,369887
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,600131
Inf
0
Best score in the node (so far): 0,805777
Inf
0
Best ordering: 40

41. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,700739
0,390093
- Best threshold by current dynamic attribute: -0,739969
-0,700739
0,390093
0,369887
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,600131
Inf
0
Best score in the node (so far): 0,805777
Inf
0
Best ordering: 41

42. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,700739
0,390093
- Best threshold by current dynamic attribute: -0,739969
-0,700739
0,390093
0,369887
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,600131
Inf
0
Best score in the node (so far): 0,805777
Inf
0
Best ordering: 42

43. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
-
CBO: CBOSimple
- Dynamic attribute selection
M
Threshold: -0,739969
-0,700739
0,390093
- Best threshold by current dynamic attribute: -0,739969
-0,700739
0,390093
0,369887
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,600131
Inf
0
Best score in the node (so far): 0,805777
Inf
0
Best ordering: 43

44. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
-
CBO: -
CBOSimple Dynamic attribute selection
M
Threshold: -0,739969
-0,700739
0,390093
- Best threshold by current dynamic attribute: -0,739969
-0,700739
0,390093
0,369887
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,600131
Inf
0
Best score in the node (so far): 0,805777
Inf
0
Best ordering: 44

45. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
-
CBO: -
CBOSimple Dynamic attribute selection
M
Threshold: -0,739969
-0,700739
0,390093
- Best threshold by current dynamic attribute: -0,739969
-0,700739
0,390093
0,369887
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,600131
Inf
0
Best score in the node (so far): 0,805777
Inf
0
Best ordering: 45

46. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
-
CBO: -
CBOSimple Dynamic attribute selection
M
Threshold: -0,739969
-0,700739
0,390093
- Best threshold by current dynamic attribute: -0,739969
-0,700739
0,390093
0,369887
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,805777
0,600131
Inf
0
Best score in the node (so far): 0,805777
Inf
0
Best ordering: 46

47. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
-
CBO: -
CBOSimple Dynamic attribute selection
M
Threshold: -0,739969
-0,700739
0,390093
- Best threshold by current dynamic attribute: -0,700739
0,390093
0,369887
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,600131
Inf
0
Best score in the node (so far): Inf
0
Best ordering: 47

48. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
ESONext100
-
CBO: -
CBOSimple Dynamic attribute selection
M
Threshold: -0,700739
0,390093
- Best threshold by current dynamic attribute: -0,700739
0,390093
0,369887
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,600131
Inf
0
Best score in the node (so far): Inf
0
Best ordering: 48

49. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
-
CBO: -
CBOSimple Dynamic attribute selection
M
Threshold: -0,700739
- Best threshold by current dynamic attribute: -0,700739
0,390093
0,369887
0,77183
2,48622
-
Score of the best split by current dyn. attribute : 0,600131
Inf
0
Best score in the node (so far): Inf
0
Best ordering: 49

50. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
-
CBO: -
CBOSimple Dynamic attribute selection
M
Threshold: -0,700739
- Best threshold by current dynamic attribute: -0,700739
0,369887
2,48622
-
Score of the best split by current dyn. attribute : Inf
0
Best score in the node (so far): Inf
0
Best ordering: 50

51. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
-
CBO: -
CBOSimple Dynamic attribute selection
M
Threshold: -0,700739
- Best threshold by current dynamic attribute: -0,700739
0,369887
2,48622
-
Score of the best split by current dyn. attribute : Inf
0
Best score in the node (so far): Inf
0
Best ordering: 51

52. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
-
CBO: -
CBOSimple Dynamic attribute selection
M
Threshold: -0,700739
- Best threshold by current dynamic attribute: -0,700739
0,369887
2,48622
-
Score of the best split by current dyn. attribute : Inf
0
Best score in the node (so far): Inf
0
Best ordering: 52

53. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
-
CBO: -
CBOSimple Dynamic attribute selection
M
Threshold: -0,700739
- Best threshold by current dynamic attribute: -0,700739
0,369887
2,48622
Score of the best split by current dyn. attribute : 0
Best score in the node (so far): 0
Best ordering: 53

54. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: ESONextMax
-
CBO: -
CBOSimple Dynamic attribute selection
M
Threshold: -0,700739
- Best threshold by current dynamic attribute: -0,700739
0,369887
2,48622
Score of the best split by current dyn. attribute : 0
Best score in the node (so far): 0
Best ordering: 54

55. LEARNING THE MODEL
Defined operators EyeShifter – Cursor position
ESONextMax
ESONextMax To the next local maxima
ESO ESONext100
ESONext100 Step forward 100
ESOMax
ESOMax To the global maxima
CBO CBSimple
CBOSimple Value at cursor position
ShiftTree
F(x)
ConditionBuilder - Ordering
Best split in node (so far)
ESO: -
CBO: - Dynamic attribute selection
M
Threshold: - Best threshold by current dynamic attribute: -0,700739
0,369887
2,48622
Score of the best split by current dyn. attribute : 0
Best score in the node (so far): 0
Best ordering: 55