Lecture19

Introduction to Machine
Learning
Lecture 19
Genetic Fuzzy Systems

Albert Orriols i Puig
http://www.albertorriols.net
htt // lb t i l t
aorriols@salle.url.edu

Artificial Intelligence – Machine Learning
g g
Enginyeria i Arquitectura La Salle
Universitat Ramon Llull

Recap of Lectures 5-18
Supervised learning
p g
Data classification
Labeled data
Build a model that
covers all the space

Unsupervised
Uns per ised learning
Clustering
Unlabeled data
Group similar objects

Association rule analysis
Unlabeled data
Get the most frequent/important associations
Slide 2
Artificial Intelligence Machine Learning

Today’s Agenda

Fuzzy Logics
Fuzzy Systems
Genetic Fuzzy Systems

Slide 3

Fuzzy Logics
Looking up in the dictionary…
gp y
Fuzzy = “not clear, distinct, or precise; blurred”
The
Th world is imprecise, not clear, blurred…
ld i i i tl bl d
The world is fuzzy!

Definition of fuzzy logics
yg
A form of knowledge representation suitable for notions that
cannot be defined precisely, but which depend upon their
p y, p p
contexts

Let’s go from true and false (traditional logics) to
something more powerful

Slide 4

Fuzzy Logics
Traditional logic representation
g p

Slow Fast

Logic rep
Slow speed = 0
Fast speed = 1

Slide 5

Fuzzy Logics
How fast is fast?
Definition of slow and fast depend on the eyes of the beholder
Natural language contains many subjective t
N t ll ti bj ti terms

How can I deal with this?
H d l ith thi ?

Fast Very fast
Very slow Slow

These four are linguistic terms
St ,
Still, I need to de e t e se a t cs o eac
eed define the semantics of each
linguistic term!
Slide 6

Fuzzy Logics
Classical view
Define intervals:
very slow [0 – 0.25]
slow [0.25 – 0.5]
fast [0.5 – 0.75]
very fast [0.75 – 1]

Fuzzy logics view
yg
Consider the degree with which each observation belongs to each
linguistic term
Define a membership function

Slide 7

Fuzzy Logics
Member ship functions
Semantics of the system

Fast Very fast
Very slow Slow

0 0.25 0.50 0.75 1

Slide 8

Fuzzy Logics
Many different membership functions. Some of them are
y p

1

c
ab d

Slide 9

Fuzzy Systems
Fuzzy systems
yy
are fundamental methodologies to represent and process
gu s c o a o
linguistic information
use fuzzy logic to either represent the knowledge or model the
interactions a d relationships a o g the sys e variables in
e ac o s and e a o s ps among e system a ab es
environments where there is uncertainty and imprecision.
E.g. of knowledge representation:
If john is tall and fast then strong

Genetic fuzzy systems
yy
The use of genetic/evolutionary algorithms (GAs) to design
fuzzy systems
yy

Slide 10

GFS

Slide 11

GFS
Two key elements:
y
Fuzzy system
In our case, we will focus on rule-based systems
case
Genetic algorithm

Fuzzy system
yy

Slide 12

Fuzzy Rule-Based Systems
Rule base
If size is small and weight is small then quality is bad
If size is small and weight i l
ii ll d i ht is large th quality i medium
then lit is di
If size is large and weight is small then quality is medium
If size is large and weight is large then quality is good
Data base

Slide 13

Operation of the inference system

Centre of
gravity

Slide 14

Great, I know how to infer… But who gives me
, g
The rules
The i f
Th information of the data base (the semantics)
ti f th d t b (th ti )
The inference engine
Inference system
Defuzzification methods

Use a genetic algorithm for this task

Slide 15

Recall GAs?

Population

Individual 1 Fit. 1
Individual 2 Fit. 2
Individual i
Population ... ...
Individual n Fit. n Individual j
Individual 1
Initialization
Individual 2 Individual 1
...
Individual n
Individual n

Individual i’
Individual i’’
Mutation Individual j’
Individual j’’
Individual
I di id l 1’
Individual 1’’
Individual n’
Individual n’’

Selection + Mutation: Continuous improvement and local search
Selection + Recombination: Innovation

Slide 16

Where Do we Use the GA?
Taxonomy of GFS (
y (Herrera, 2008)
, )

Slide 17

Where Do we Use the GA?
Taxonomy of GFS (
y (Herrera, 2008)
, )

Slide 18

Topics
We are going to see
g g
Genetic tuning
1.

Genetic rule learning
G ti ll i
2.

Genetic rule selection
3.

Genetic DB learning
4.

S u ta eous genetic ea
Simultaneous ge et c learning o KB co po e ts
g of components
5
5.

Genetic learning of KB components and inference engine
6.
pa a ete s
parameters

1st seen i thi l t
in this lecture. 2nd-5th seen i next l t
5 in t lecture
Information based on the paper Herrera (2009) and the
corresponding presentation
di t ti

Slide 19

1. Genetic Tuning
Typically membership functions
yp y p
are defined by domain experts
are j t selected f
just l t d from general f
l forms: triangles, t
ti l trapezoids,
id
Gaussian…
But,
B t could we have better membership functions?
ld h b tt b hi f ti ?
Let a GA tune the membership functions

Also, tune the inference parameters

Slide 20

1. Genetic Tuning
How do we
apply the GA?

So, we are modifying
the partitions of the
feature space

Slide 21

1. Genetic Tuning
An example: Tuning triangular membership functions
p g g p
Each chromosome encodes a different DB definition
2 vars x 3 ling. labels = 6 mem. functions
g
Triangles 3 real values to code them
Chromosome length = 18 genes

Note that the RB remains unchanged!
Slide 22

Next Class

Next l
N t class
Genetic rule learning
1.

Genetic rule selection
2.

Genetic DB learning
3.

Simultaneous genetic learning of KB components
4.

Genetic learning of KB components and inference engine
G ti l i f t di f i
5.
parameters

Applications

Slide 23

Lecture19

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