Modelling the Initialisation Stage of the ALKR Representation for Discrete Domains and GABIL Encoding

Modelling the Initialisation Stage of the ALKR
Representation for Discrete Domains and
GABIL Encoding

María A. Franco, Natalio Krasnogor, Jaume Bacardit

University of Nottingham, UK.
ASAP Research Group,
School of Computer Science
mxf@cs.nott.ac.uk

July 14, 2011

Franco et al. (University of Nottingham) Modelling Initialisation using ALKR+GABIL July 14, 2011 1 / 25

Problem deﬁnition

BioHEL[Bacardit et al., 2009a] is a Genetic Based Machine
Learning (GBML) designed to cope with large scale
datasets[Bacardit et al., 2009b].
Iterative Rule Learning approach
Attribute List Knowledge Representation (ALKR)
ILAS Windowing scheme
Default rule
Smart initialisation mechanisms (covering)
GPU-based evaluation process

Problem
The system obtains good results [Stout et al., 2008], but we do not
have a formal understanding of why, when and how this happens.


What is the aim of this work?
The aim of this work is to model the initialisation stage of the BioHEL
system and calculate the probability of having a good initial
population. Two conditions should be meet[Goldberg, 2002]:
A good individual exists in an initial population (building blocks)
The initial population covers the whole search space

Background
These probabilities are also know as schema and covering bound.
This have already being determined for XCS and the ternary
representation {1,0,#} by [Butz, 2006].

Problem
Models need to be adapted for our ALKR+GABIL representation.
Moreover, we want to model the impact of the BioHEL mechanisms
that are relevant in initialisation: covering and default rule.


1 Background
GABIL Representation
Attribute List Knowledge Representation (ALKR)

2 Probabilistic models
Initial considerations
Schema bound
How does the overlapping affects?
Covering bound

3 Generalised model for x-ary attributes
Schema and Covering bound

4 Conclusions and Further Work


How does GABIL works?

The GABIL representation[Jong and Spears, 1991] is used inside
ALKR to represent nominal attributes.
Example
F1 ={A,B,C} F2={O,P} F3={W,Z,X,Y}
F1 F2 F3
100 01 1101

F1 is A ∧ F2 is P ∧ (F3 is W ∨ F3 is Z ∨ F3 is Y)

In GABIL, when initialising the attribute values we set the bit to 1 with
probability p and to 0 with probability 1 − p


How does Attribute List Knowledge Representation works?

ALKR Classiﬁer Example

numAtt 3
whichAtt 0
predicates 0.5 0.7 0.3

oﬀsetPred 0
class 1

How do we select the attributes in the list?
1 d <= ExpAtts
ld = ExpAtts
d d > ExpAtts


Initial considerations for the probabilistic models

Mechanisms involved in initialisation

Covering ⇒ We have to consider 4
initialisation scenarios
Default Rule

Types of attributes
Fully mapped attributes
Partially mapped attributes.


Schema bound

Problem
We want to calculate the probability of having good classifiers or
representatives in an initial population. Classifiers that do not make
mistakes, since they represent correctly all the specified bits in an
original problem rule.

Example
Considering the rule #10#1 with 3 values specified (k=3), the following
classifiers are representatives: 110*1, 11011, 010*1.


Schema bound

Question
What is the probability of obtaining a representative with at least k
values speciﬁed?

To become a representative the rule should:
1 Specify at least k attributes correctly.
2 The rest of the attributes should not have all 0’s.

k d−k
2 f (ld p(1−p))k (1−ld (1−p)2 )
P(rep) =

where kf is the number of fully map attributes


Schema bound

Question
values speciﬁed?


Without using any of the mechanisms:

k d−k
2 f (ld p(1−p))k (1−ld (1−p)2 )
P(rep) = n



Schema bound

Question
values speciﬁed?


Using default rule:

k d−k
2 f (ld p(1−p))k (1−ld (1−p)2 )
P(rep) = n−1



Schema bound

Question
What happens when we use covering?

1 We sample an instance with uniform probabilities for all classes.
2 We set the bits corresponding to the instance values to 1.
It is not possible to have all 0’s anymore.

P(rep) = m
(ld (1 − p))k

where m is the number of classes mapped by the problem rules


Schema bound

Question
What happens when we use covering?


P(rep) = m
n (ld (1 − p))k



Schema bound

Question
What happens when we use covering and default rule?


P(rep) = m
n−1 (ld (1 − p))k



Problems used for model validation

Binary and Ternary Multiplexer problems
k address bits
2k string bits (3k for ternary case)

k-Disjuntive Normal Functions
[Butz and Pelikan, 2006, Franco et al., 2010].
r disjunctive terms
d possible attributes
k represented attributes in each term

Example kDNF: d = 10, k = 3, r = 3

(¬x1 ∧ x5 ∧ x7 ) ∨ (x1 ∧ ¬x2 ∧ x8 ) ∨ (x4 ∧ ¬x5 ∧ ¬x9 )


Schema bound - Model validation
0.8 1
Teoretical p=0.75 Teoretical p=0.75
0.7 Empirical p=0.75 Empirical p=0.75
Empirical p=0.50 0.8 Empirical p=0.50
0.6
Empirical p=0.25 Empirical p=0.25
0.5 0.6
P(rep)

P(rep)
0.4

0.3 0.4

0.2
0.2
0.1

0 0
0 2 4 6 8 10 0 2 4 6 8 10
k - Number of Attributes k - Number of Attributes

(a) MUX - No Covering (b) MUX- Covering

0.1 1
0.08 Teoretical p=0.50 0.8 Teoretical p=0.50
0.06 Empirical NoDef p=0.75 0.6 Empirical NoDef p=0.75
P(rep)

P(rep)
Teoretical NoDef p=0.75 Teoretical NoDef p=0.75
Empirical NoDef p=0.50 Empirical NoDef p=0.50
0.04 Teoretical NoDef p=0.50 0.4 Teoretical NoDef p=0.50
Empirical NoDef p=0.25 Empirical NoDef p=0.25
Teoretical NoDef p=0.25 Teoretical NoDef p=0.25
0.02 0.2

0 0
0 2 4 6 8 10 0 2 4 6 8 10
k - Number of attributes especified k - Number of attributes especified

(c) kDNF - No Covering (d) kDNF - Covering


What have we calculated so far?

These models so far only hold for:

Problems with Problems that have just
no-overlapping one rule


What happens here?


How does the overlapping affects the probability of a
representative?

P(rep)
P(niche) =
r
1 ExamplesNiche (EN)
=
? ExamplesCovered (EC)
r
EC = 2d 1 − 1 − 2−k

2d
EN =
2k

P (rep) = 1 − (1 − P(niche))r


representative?

P(rep)
P(niche) =
?
=
r
EC = 2d 1 − 1 − 2−k

2d
EN =
2k

P (rep) = 1 − (1 − P(niche))r


representative?

P(rep)
P(niche) =
2k 1 − (1 − 2−k )r

=
r
EC = 2d 1 − 1 − 2−k

2d
EN =
2k
P (rep) = 1 − (1 − P(niche))r


Validation of models considering overlapping

Teoretical Teoretical
P(rep) Empirical r=1 P(rep) Empirical r=1
Empirical r=5 Empirical r=5
1 Empirical r=10 1 Empirical r=10
Empirical r=20 Empirical r=20
0.8 Empirical r=40 0.8 Empirical r=40
0.6 0.6
0.4 0.4
0.2 0.2
0 0
25 25
0 2 5 0 2 5
4 # of rules 4 # of rules
Atts esp (k) 6 8 Atts esp (k) 6 8
10 1 10 1

(e) Base Case (f) Covering and Default Class


Covering bound

Problem
How can we calculate the probability of covering the whole search
space?

We need to calculate the probability of matching an instance

d
Base case P(match) = (1 − ld + ld p)

d
1+p
Covering case P(match) = 1 − ld + ld 2


Covering bound - Model validation

(g) No covering (h) Covering

1 1
Model p=0.75 Model p=0.75
0.8 Model p=0.50 0.8 Model p=0.50
0.6 0.6
P(match)

P(match)
0.4 0.4

0.2 0.2

0 0
0 5 10 15 20 0 5 10 15 20


What happens with x-ary attributes?

What happens when the problem is not binary but has more than 2
values per attribute?
Generalised models for x-ary attributes
Where t is the number of values per attribute and e is the number of
active bits per attribute.

Example 1: 101|110|011:0 ⇒ t=3 e=2
Example 2: 001|100|010:1 ⇒ t=3 e=1


Generalised model for x-ary attributes

Schema bound
k d−k
tkf (ld pe (1−p)t−e ) (1−ld (1−p)t )
Base case P(rep) = n

k
m t−e−1
Covering case P(rep) = n ld pe−1 (1 − p)

Covering bound

d

d
1+(t−1)p
Covering case P(match) = 1 − ld + ld t



Schema bound with Default Rule
k d−k
tkf (ld pe (1−p)t−e ) (1−ld (1−p)t )
Base case P(rep) = n−1

k
m t−e−1
Covering case P(rep) = n−1 ld pe−1 (1 − p)

Covering bound

d

d
1+(t−1)p
Covering case P(match) = 1 − ld + ld t



Schema bound validation (with ternary multiplexer problems)

(i) No covering (j) Covering

0.16 0.6
0.14 Model p=0.75 Model p=0.75
Empirical p=0.50 0.5 Empirical p=0.50
0.12 Model p=0.50 Model p=0.50
Model p=0.25 0.4 Model p=0.25
0.1
P(rep)

P(rep)
0.08 0.3

0.06
0.2
0.04
0.1
0.02

0 0
1 2 3 4 5 6 1 2 3 4 5 6

≈ 5 times more probability of generating
a good individual when using covering



Covering bound validation (with ternary multiplexer problems)

(k) No covering (l) Covering

1 1
0.8 Model p=0.50 0.8 Model p=0.50
0.6 0.6
P(match)

P(match)
0.4 0.4

0.2 0.2

0 0
2 4 6 8 10 12 14 2 4 6 8 10 12 14


Conclusions

The presented models explains what is the probability of having
a good initial population in BioHEL considering de ALKR
representation and other initialisation mechanisms.
We also presented a generalisation of the model for x-ary
attributes and adjusted the probability for problems with
overlapping.
These models explain the beneﬁts of BioHEL initialisation
mechanisms giving a further understanding of how the BioHEL
system works.


Further Work

Simplify the current models to make them less dependent on
problem parameters not known beforehand.
Model the reproductive opportunity and learning time of BioHEL.
Derive boundaries for the population size and other user-deﬁned
parameters in BioHEL.


Modelling the Initialisation Stage of the ALKR
Representation for Discrete Domains and
GABIL Encoding

María A. Franco, Natalio Krasnogor, Jaume Bacardit

University of Nottingham, UK.
ASAP Research Group,
School of Computer Science
mxf@cs.nott.ac.uk

July 14, 2011


Bacardit, J., Burke, E., and Krasnogor, N. (2009a).
Improving the scalability of rule-based evolutionary learning.
Memetic Computing, 1(1):55–67.

Bacardit, J., Stout, M., Hirst, J. D., Valencia, A., Smith, R., and Krasnogor, N. (2009b).
Automated alphabet reduction for protein datasets.
BMC Bioinformatics, 10(1):6.

Butz, M. V. (2006).
Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design, volume 109 of
Studies in Fuzziness and Soft Computing.
Springer.

Butz, M. V. and Pelikan, M. (2006).
Studying XCS/BOA learning in boolean functions: structure encoding and random boolean functions.
In GECCO ’06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 1449–456,
New York, NY, USA. ACM.

Franco, M. A., Krasnogor, N., and Bacardit, J. (2010).
Analysing biohel using challenging boolean functions.
In GECCO ’10: Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, pages
1855–1862, New York, NY, USA. ACM.

Goldberg, D. E. (2002).
The Design of Innovation: Lessons from and for Competent Genetic Algorithms.
Kluwer Academic Publishers, Norwell, MA, USA.

Jong, K. D. and Spears, W. M. (1991).
Learning concept classiﬁcation rules using genetic algorithms.
In Proceedings of the 12th international joint conference on Artiﬁcial intelligence - Volume 2, pages 651–656, Sydney,
New South Wales, Australia. Morgan Kaufmann Publishers Inc.

Stout, M., Bacardit, J., Hirst, J. D., and Krasnogor, N. (2008).
Prediction of recursive convex hull class assignments for protein residues.
Bioinformatics, 24(7):916–923.


Modelling the Initialisation Stage of the ALKR Representation for Discrete Domains and GABIL Encoding

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Andere mochten auch

Andere mochten auch (6)

Ähnlich wie Modelling the Initialisation Stage of the ALKR Representation for Discrete Domains and GABIL Encoding

Ähnlich wie Modelling the Initialisation Stage of the ALKR Representation for Discrete Domains and GABIL Encoding (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Modelling the Initialisation Stage of the ALKR Representation for Discrete Domains and GABIL Encoding