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Using Interactive Evolution for Exploratory Data Analysis

Using Interactive Evolution for Exploratory Data Analysis. Tomáš Řehořek Czech 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

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Using Interactive Evolution for Exploratory Data Analysis

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  1. Using Interactive Evolution for Exploratory Data Analysis Tomáš Řehořek Czech Technical University in Prague

  2. CIG Research Group • Czech Technical University in Prague • Faculty of Electrical Engineering (FEL) • Faculty of Information Technology (FIT)

  3. CIG Research Group • Data Mining • Algorithms, Visualization, Automation • Biologically inspired algorithms • Evolutionary computation • Artificial neural networks • Artificial Intelligence • Machine learning, Optimization

  4. Optimization in Data Mining • Main objective of the CIG research group ArtificialNeural Networks Evolutionarycomputation Data Mining Machinelearning ArtificialIntelligence Optimization

  5. 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

  6. Interactive Evolutionary Computation (IEC) • Evolutionary Computation using human evaluation as the fitness function • Currently used almost exclusivelyfor artistic purposes • Images, Sounds, Animations… • Inspiration: http://picbreeder.org

  7. Interactive Evolution PicBreeder by Jimmy Secretan Kenneth Stanley

  8. Next generation … and so on …

  9. And after 75 generations ... ... you eventually get something interesting

  10. The technology hidden behind x grayscale z z x Neural net draws the image

  11. Neuroevolution x grayscale z By clicking, you increase fitness of nets Next generations inherit fit building patterns

  12. Gallery of discovered images

  13. Using Interactive Evolutionin Exploratory Data Analysis • Experiment with evolvingprojections Examples inn-dimensionalspace 2D

  14. Interactive Evolution of Projections Candidateprojections Machine        Feedback Feedback Human

  15. Interactive Evolution of Projections Candidateprojections Machine      Feedback Feedback Human

  16. Data Projection Experiments • Linear transformation • Evolve coefficient matrix • Do the transformation using formula: … resulting a point in 2D-space

  17. Data Projection Experiments • Sigmoidal transformation • Evolve coefficient matrix • Do the transformation using formula: b a c

  18. Experiments with Wine Dataset PCA SOM

  19. Separation of Different Classes using Linear Projection

  20. Separation of Different Classes using Sigmoidal Projection

  21. There are many possible goals! „Blue points down“ – 5 generations, sigmoid projection Outlier Detection – 8 generations, linear projection

  22. 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

  23. Thank you for your attention! Tomáš Řehořek tomas.rehorek@fit.cvut.cz Computational Intelligence Group (CIG) Faculty of Information Technology (FIT) Czech Technical University (CTU) in Prague

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