Decision Trees and Random Forests Modeling by using P Systems Jos - - PowerPoint PPT Presentation

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Decision Trees and Random Forests Modeling by using P Systems Jos - - PowerPoint PPT Presentation

19 th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany Decision Trees and Random Forests Modeling by using P Systems Jos M. Sempere Department of Information Systems and Computation Universitat


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Decision Trees and Random Forests Modeling by using P Systems

jsempere@dsic.upv.es http://personales.upv.es/jsempere José M. Sempere Department of Information Systems and Computation Universitat Politècnica de València

19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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Goals of this work: (1) Provide algorithms to define P systems from decision trees and random forests (2) Introduce machine learning techniques to obtain decision trees and random forests from given data through membrane and object rules based on P systems working in entropic/functional manner Previous Works

  • Decision Tree Models Induced by Membrane Systems (2015) J. Wang, J. Hu, M.J. Pérez-Jiménez, A.Riscos-Núñez

ROMJIST Vol.18 No.3 pp 228-239

  • Self-constructing Recognizer P Systems. D. Díaz-Pernil, F. Peña-Cantillana, M.A. Gutiérrez-Naranjo. In Proceedings of

the Thirteenth Brainstorming Week on Membrane Computing , pp 137-154. Fenix Editora. 2014. Díaz-Pernil et al. Wang et al. Our approach Trees defined by the membrane structure Tree-like objects Trees defined by the membrane structure Non-deterministic search of structures External Induction Algorithm Algorithm runs by P rules within an entropic manner 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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A Decision Tree is a representation for a discrete-valued function !: #$×#&× ⋯×#( → * #$

+,-./(#$. 2, 2$) +,-./(#$. 2, 25)

#& #6

+,-./(#6. 2, 2$) +,-./(#6. 2, 27)

*. 2$ *. 26

+,-./ #6. 2, 28 ∈ {#6. 2 = 28, #6. 2 ≤ 28, #6. 2 ≥ 28, #6. 2 ≠ 28, …} 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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An example: protein-protein interactions prediction

(From “What are decision trees ?”. Carl Kingsford & Steven L. Salzberg. Nature Biotechnology 26 No. 9, 1011 – 1013 (2008) )

19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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Implementing decision trees by membrane structures (I) Objects alphabet 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany Membrane labels alphabet

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Implementing decision trees by membrane structures (I) 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany An algorithm to translate decision trees to P systems

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Implementing decision trees by membrane structures (I) 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany An algorithm to translate decision trees to P systems

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Implementing decision trees by membrane structures (I) 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany An algorithm to translate decision trees to P systems

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Implementing decision trees by membrane structures (I) 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany An algorithm to translate decision trees to P systems

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Implementing decision trees by membrane structures (II) ECYes ECNo SFYes SFNo SCLNo SCLYes GDL5Yes GDL5No 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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Implementing decision trees by membrane structures (III) ECYes ECNo SFYes SFNo SCLNo SCLYes GDL5Yes GDL5No

∀ ", $, % ∈ '(), *+ ,-'().-/".0$12/3% → [.0$],-7() ∀ ", $, % ∈ '(), *+ ,-*+.-/".0$12/3% → [.-/"12/3%],-8+

19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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Implementing decision trees by membrane structures (IV) ECYes ECNo SFYes SFNo SCLNo SCLYes GDL5Yes GDL5No

∀ ", $, % ∈ '(), *+ ,-'().-/".0$12/3% → [.0$],-7() ∀ ", $, % ∈ '(), *+ ,-*+.-/".0$12/3% → [.-/"12/3%],-8+

12/3'()→ [7,.]12/37() 12/3*+→ [89]12/38+

19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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Implementing decision trees by membrane structures (V) ECYes ECNo SFYes SFNo SCLNo SCLYes GDL5Yes GDL5No

∀ ", $, % ∈ '(), *+ ,-'().-/".0$12/3% → [.0$],-7() ∀ ", $, % ∈ '(), *+ ,-*+.-/".0$12/3% → [.-/"12/3%],-8+

12/3'()→ [7,.]12/7() 12/3*+→ [89]12/8+

[YES] → [ ] YES [NO] → [ ] NO [NO] → [ ] NO

[NO] → [ ] NO [YES] → [ ] YES

[YES] → [ ] YES [NO] → [ ] NO

[NO] → [ ] NO [YES] → [ ] YES

19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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A parsing example ECYes ECNo SFYes SFNo SCLNo SCLYes

GDL5Yes GDL5No

ECno SFyes SCLyes GDL5no 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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A parsing example ECYes ECNo SFYes SFNo SCLNo SCLYes

GDL5Yes GDL5No

SCLyes GDL5no 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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A parsing example ECYes ECNo SFYes SFNo SCLNo SCLYes

GDL5Yes GDL5No

GDL5no 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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A parsing example ECYes ECNo SFYes SFNo SCLNo SCLYes

GDL5Yes GDL5No

NO 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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A parsing example ECYes ECNo SFYes SFNo SCLNo SCLYes

GDL5Yes GDL5No

NO 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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A parsing example ECYes ECNo SFYes SFNo SCLNo SCLYes

GDL5Yes GDL5No

NO 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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A parsing example ECYes ECNo SFYes SFNo SCLNo SCLYes

GDL5Yes GDL5No

NO 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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A parsing example ECYes ECNo SFYes SFNo SCLNo SCLYes

GDL5Yes GDL5No

NO 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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Applying a machine learning technique inside a P system

Sample ! " # Decision 1 High High High Yes 2 High High High Yes 3 Low High Low Yes 4 Medium High High Not

19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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Applying a machine learning technique inside a P system

Sample ! " # Decision 1 High High High Yes 2 High High High Yes 3 Low High Low Yes 4 Medium High High Not !$%&'

(

"$%&'

(

#$%&'

(

)*+%,%-."/0

(

!$%&'

1

"$%&'

1

#$%&'

1

)*+%,%-."/0

1

!2-3

4

"$%&'

4

#2-3

4

)*+%,%-."/0

4

!5*6%78

9

"$%&'

9

#2-3

9

)*+%,%-.:;

9

19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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Applying a machine learning technique inside a P system

Sample ! " # Decision 1 High High High Yes 2 High High High Yes 3 Low High Low Yes 4 Medium High High Not !$%&'

(

"$%&'

(

#$%&'

(

)*+%,%-."/0

(

!$%&'

1

"$%&'

1

#$%&'

1

)*+%,%-."/0

1

!2-3

4

"$%&'

4

#2-3

4

)*+%,%-."/0

4

!5*6%78

9

"$%&'

9

#2-3

9

)*+%,%-.:;

9

A generic algorithm to build decision trees from data

Input: A finite set of supervised tuples E Output: A decision tree T Method: 1) Create an arbitrary root 2) If all the tuples belong to class <= then return(root, <=) else 1. Select an attribute ! with values >(, >1, … , >5 2. Make a partition of E according to the attribute value /(, /1, … , /5 ∶ / = ⋃%D(

5 /%

3. Build decision trees for every subset: E(, E1, … , E5 endMethod

FGGH

I = JK I = JL I = JM N

K

NL NM

19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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Applying a machine learning technique inside a P system

Sample ! " # Decision 1 High High High Yes 2 High High High Yes 3 Low High Low Yes 4 Medium High High Not !$%&'

(

"$%&'

(

#$%&'

(

)*+%,%-."/0

(

!$%&'

1

"$%&'

1

#$%&'

1

)*+%,%-."/0

1

!2-3

4

"$%&'

4

#2-3

4

)*+%,%-."/0

4

!5*6%78

9

"$%&'

9

#2-3

9

)*+%,%-.:;

9

A generic algorithm to build decision trees from data

Input: A finite set of supervised tuples E Output: A decision tree T Method: 1) Create an arbitrary root 2) If all the tuples belong to class <= then return(root, <=) else 1. Select an attribute ! with values >(, >1, … , >5 2. Make a partition of E according to the attribute value /(, /1, … , /5 ∶ / = ⋃%D(

5 /%

3. Build decision trees for every subset: E(, E1, … , E5 endMethod

Suppose that the attribute for the node root is X, then we apply the following three rules (membrane creation)

!$%&'

(

"$%&'

(

#$%&'

(

)*+%,%-."/0

(

!$%&'

1

"$%&'

1

#$%&'

1

)*+%,%-."/0

1

→ [ "$%&'

(

#$%&'

(

)*+%,%-."/0

(

"$%&'

1

#$%&'

1

)*+%,%-."/0

1

]!$%&' !2-3

4

"$%&'

4

#2-3

4

)*+%,%-."/0

4

→ [ "$%&'

4

#2-3

4

)*+%,%-."/0

4

]!2-3 !5*6%78

9

"$%&'

9

#2-3

9

)*+%,%-.:;

9

→ [ "$%&'

9

#2-3

9

)*+%,%-.:;

9 ]!5*6%78

19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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Applying a machine learning technique inside a P system How can the appropriate rules be selected ?

  • External algorithm selects the rule and we apply a translation scheme from decision trees to P systems
  • Apply a P system working within an entropic manner (CMC17)
  • Apply rules according to a functional criterium: (FUNCTIONAL WORKING MANNER !!!)

!: # $ → & 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany '(, … , '+ : ' → , A function defines the selections of the rules (instead of selection by priority) Every rule has some parameters (elements from V) and the function ! is calculated over them. The rule to be applied has the highest value for the function !.

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Random forests

19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

FINAL DECISION input data decision trees ensemble decision criteria

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From decision trees to random forests

Random Forest algorithm

mtry: number of features for node splitting ntree: number of trees in the forest

19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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From cell-like P systems to tissue-like P systems

Initially we have only one cell with cell-creation rules and all the input data

!" #$ … &' → !" #$ … &'(!" #$)

!" #$ … &'

!" #$ … &' → !" #$ … &'(#$ &')

19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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From cell-like P systems to tissue-like P systems

Initially we have only one cell with cell-creation rules and all the input data

!" #$ … &' → !" #$ … &'(!" #$)

!" #$ … &' !" #$ … &' !" #$

!" #$ … &' → !" #$ … &'(#$ &')

#$ &' 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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From cell-like P systems to tissue-like P systems

Initially we have only one cell with cell-creation rules and all the input data

!" #$ … &' → !" #$ … &'(!" #$)

!" #$ … &' !" #$ … &' !" #$

!" #$ … &' → !" #$ … &'(#$ &')

#$ &' Apply rules to create a decision tree 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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YES YES YES NO YES NO

YES → (#$%)' NO → (())'

Rules inside every P system at skin region (communication rules) * 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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YES YES YES NO YES NO ! 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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YES YES YES NO YES NO ! Majority rules ##$% > #'( → #$%*+, #'( > ##$% → '(*+, 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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YES ! Majority rules ##$% > #'( → #$%*+, #'( > ##$% → '(*+, 19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

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19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany

Conclusions (... work in progress)

  • A definition of decision trees by P systems
  • Introducing machine learning approach by P systems working in an entropic/functional manner
  • A definition of random forests by (tissue) P systems
  • Introducing ensemble decision criteria by (majority) rules
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19th International Conference on Membrane Computing (CMC19) 4-7 September 2018 , Dresden, Germany