Ulf Johansson

Associate Professor

I have, since 1999, been working on scientific problems that can be broadly described as machine learning techniques for data analysis. In my thesis, I suggested two novel data mining algorithms based on Genetic Programming. I have contributed with theoretical results, methods, systems, algorithms and applications within the fields of data mining, soft computing and machine learning. Some key results include:

  • · An algorithm (G-REX) for rule extraction from opaque models
  • · Several algorithms for ensemble creation; e.g., GEMS
  • · An algorithm (Chipper) for rule learning
  • · Novel algorithms based on lazy learning (BuLL and G-kNN)
  • · Novel algorithms based on machine learning and soft computing techniques utilizing and extending the conformal prediction framework
  • · A novel methodology, named oracle coaching, used for building interpretable yet accurate models
  • · A novel methodology, based on a suggested concept named imaginary ensembles, used for selecting a specific classifier from a large pool
  • · Methods and theoretical findings regarding neural networks
  • · Methods and theoretical findings regarding ensembles
  • · Methods and theoretical findings regarding evolutionary computation
  • · Methods and theoretical findings regarding the concept description task
  • · Methods and theoretical findings regarding conformal prediction
  • · Theoretical findings and new criteria suggested for the rule extraction task
  • · Theoretical findings regarding the relationship between accuracy and diversity in ensembles
  • · Novel micro-techniques suggested within the fields of neural networks, genetic programming, ensembles, rule learning, lazy learning, feature selection and time series forecasting
  • · Applications within, for instance, drug discovery, health science, marketing, high-frequency trading, game AI, sales forecasting and gambling


My publications can be found at Google Scholar

Title of Dissertation

Obtaining Accurate and Comprehensible Data Mining Models: An Evolutionary Approach