interactive evolution in automated knowledge discovery tomáš Řehořek march 2011
TRANSCRIPT
Interactive Evolutionin Automated Knowledge Discovery
Tomáš ŘehořekMarch 2011
Knowledge Discovery Automation
• Our goal:– Given input dataset, automatically construct
KF and offer output knowledge that the user is satisfied with
– Create such a system is a big deal!
AutomatedKnowledge Discovery
Knowledge Discovery Automation
• What is Knowledge Discovery?– Transformation of input data to human-
interpretable knowledge– Oriented graph of actions (Knowledge Flow)
is a suitable approach
Knowledge Discovery Ontology
• Ontology (definition)– Formal representation of a domain– Specification of entities, their properties and
relations– Provides a vocabulary, which can be used to
model a domain• E.g.: dataset, model, testing sample, scatter plot,
confusion matrix, association rule…
Knowledge Discovery Ontology
• Ontology design problems in KD:– Which KFs are reasonable?– How should the output report look like?– May the metadata be helpful?– Are the some categories of users with similar
interests?
• Two ideas concerning Ontology:– Deductive approach– Inductive approach
Knowledge Discovery Ontology
• Deductive approach:– Ontology is given– Based on the Ontology, and the given
dataset, try to construct appropriate KF
Knowledge Discovery Ontology• Deductive approach:
Taken from: M. Žáková, P. Křemen, F. Železný, Nada Lavrač: Automating Knowledge Discovery Workflow Composition Through Ontology-Based Planning (2010)
Knowledge Discovery Ontology• Inductive approach:
– No prior assumptions about the Ontology– Learn the Ontology based on a database of
KFs designed by experts
Meta-Knowledge Discovery
DiscoveredKD Ontology
Our Approach:
Revolutionary Reporting
• There may be thousands of useful KFs– Different datasets may require different
actions– Different users may require different
knowledge
• Maybe, users form clusters:– „DM Scientist“ – may experiment with different
algorithms on a given dataset– „Business Manager“ – may appreciate
beer-and-diapers rule
• Let’s design a system capable of learning what do users like!– Adopt Interactive Evolutionary Computation– Collect feedback to evaluate fitness
• of a given KF,• for a given user,• on a given dataset,
– Store the feedback, along with the metadata, to a database
– As the DB grows, offer intelligent KF mutation based on the experience
Our Approach:
Revolutionary Reporting
• Interactive Evolutionary Computation (IEC)– Also known as „Aesthetic Selection“– Evolutionary Computation using Human
evaluation as fitness function
• Inspiration: http://picbreeder.org
Our Approach:
Revolutionary Reporting
PicBreeder
Jimmy Secretan
Kenneth Stanley
Interactive Evolution
by
Next
generation
…
and so on
…
And after 75 generations ...
... you eventually get something interesting
The technology hidden behind
x
z
grayscale
x
z
Neural net draws the image
Neuroevolution
grayscale
By clicking, you increase fitness of nets
Next generations inherit fit building patterns
x
z
Gallery of discovered images
Collaborative evolution
You start your evolution,
where others finished …
… and when discover
something interesting …
… you store it to database.
System core
ExperienceDatabase
Feedback
User
Our Approach:
Revolutionary Reporting
First Experiments: Data Projection
• Transform input Dataset to 2D
• Similar to PCA, Sammon projection etc.
: nf 2
Examples inn-Dimensional
space
2D
Experiment Setup
User
Web Client
AJAXGoogle API
Tomcat Server
Feedback Collection GUI
RapidMiner 5
jabsorbJSON-RPC(via HTTP)
MySQL
Genetic Algorithm
CurrentPopulation
Feedback
Data Projection Experiments
• Linear transformation– Evolve coefficient matrix
– Do the transformation using formula:
… resulting a point in 2D-space
1 2 n
1 2 n
, , ,
, , ,
a a a
b b b
f a x b xxn n
i i i ii=1 i=1,
[ Demonstration ]
Data Projection Experiments• Sigmoidal transformation
– Evolve coefficient matrix
– Do the transformation using formula:
a a a b b b c c c
a a a b b b c c c1,1 1,2 1,n 1,1 1,2 1,n 1,1 1,2 1,n
2,1 2,2 2,n 2,1 2,2 2,n 2,1 2,2 2,n
, , , , , , , , , , ,
, , , , , , , , , , ,
+ +
b x c b
a af x
1,i i 1,i 2,i i 2,i
n n1,i 2,i
x ci=1 i=11 e 1 e,
a
b
c
Interactive Evolution: Issues• Fitness function is too costly:
– GA requires a lot of evaluations– User may get annoyed, bored, tired…
• Heuristic approach needed to speed up the evolution!– „Hard-wired“ estimation of projection quality
• E.g. Clustering homogenity, separability,intra-cluster variability…
• Puts a limitation on what „quality“ means!
– Modeling user’s preferences…?
Surrogate Model
• Optimization approach in areas where evaluation is too expensive
• Builds an approximation model of the fitness function
• Given training dataset of so-far-known candidate solutions and their fitness…
• …predicts fitness of newly generated candidates
Surrogate Model
1. Collect fitness of an initial sample
2. Construct Surrogate Model
3. Search the Surrogate Model• Surrogate Model is cheap to evaluate• Genetic Algorithm may be employed
4. Collect fitness at new locations foundin step 3.
5. If solution is not good enough, go to 2.
Evaluating Fitness
• In order to construct fitness-prediction models, training dataset must be delivered
• Information about fitness provided by the user is indirect– In scope of single population, good projection
is sure better than bad one– However, better is a relative term– Is good projection in generation #2 better than
bad projection in generation #10…?
Interconnecting generations• In each generation, population may be
divided to up to 3 categories:– bad, neutral, good
• Let’s copy the best projection to the next-epoch population– So-called elitism in Evolutionary Computation– In scope of new population, the elite will again
fall in one of these 3 categories– This gives us information about
cross-generation progress!
Generation #1
Absolutizing Fitness
Generation #2
Equivalence relation
Partial order relation
Equivalence classesAbsolutizing Fitness
Generation #3
Fitness Prediction KF in RM
Training dataset
Current population
Normalization
Learning (3NN)
Fitnessprediction
