Using Interactive Evolution for Exploratory Data Analysis

Tomáš ŘehořekCzech Technical University in Prague

CIG Research Group

Czech Technical University in Prague Faculty of Electrical Engineering (FEL) Faculty of Information Technology (FIT)

CIG Research Group

Data Mining Algorithms, Visualization, Automation

Biologically inspired algorithms Evolutionary computation Artificial neural networks

Artificial Intelligence Machine learning, Optimization

Optimization in Data Mining Main objective of the CIG research group

DataMining

Evolutionarycomputation

ArtificialIntelligence

Optimization

Machinelearning

ArtificialNeural Networks

Dimensionality Reduction and Visualization in Data Mining Linear projections

Principal Component Analysis (PCA) Linear Discriminant Analysis (LDA)

Non-linear projections Multidimensional Scaling (MDS) Sammon Projection Kernel PCA

Interactive Evolutionary Computation (IEC) Evolutionary Computation using human

evaluation as the fitness function Currently used almost exclusively

for artistic purposes Images, Sounds, Animations…

Inspiration: http://picbreeder.org

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

Using Interactive Evolutionin Exploratory Data Analysis Experiment with evolving

projections : nf 2

Examples inn-dimensional

space

2D

Interactive Evolution of Projections

Machine

Human

Candidateprojections

FeedbackFeedback

Interactive Evolution of Projections

Machine

Human

Candidateprojections

Feedback

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,

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

Experiments with Wine Dataset

PCA SOM

Separation of Different Classes using Linear Projection

Separation of Different Classes using Sigmoidal Projection

There are many possible goals!

„Blue points down“ – 5 generations, sigmoid projection

Outlier Detection – 8 generations, linear projection

Conclusion

Interactive Evolution can be used in Exploratory Data Analysis

Our experiments show that complex projections can be easily evolved

In future, we plan to investigate such evolution in fields of Data Mining other than EDA

Thank you for your attention!

Tomáš Řehořektomas.rehorek@fit.cvut.cz

Computational Intelligence Group (CIG)

Faculty of Information Technology (FIT)

Czech Technical University (CTU) in Prague