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

Publications

My publications can be found at Google Scholar

Researcher's publications in DiVA (Digitala Vetenskapliga Arkivet)

Doctoral thesis title

Obtaining Accurate and Comprehensible Data Mining Models: An Evolutionary Approach