Special Issue 'Online Fuzzy Machine Learning and Data Mining' (Information Sciences)

Description

This special issue intends to investigate the relationship between fuzzy set theory and ML/DM with special emphasis on (but not restricted to) a particular class of approaches within the field of Fuzzy ML-DM dealing with on-line, incremental learning methods. The aim is to investigate incremental adaptation of the model parameters and the evolution of the model as cornerstone elements of techniques dedicated to dynamically changing environments over time and space. Typically, data streaming exemplifies dynamic systems (with changing operation conditions and system characteristics) which can be found in various industrial and rich-data applications (e.g. control, robotics, web, etc.). Fuzzy learning models for such systems depart from the idea that memory cannot suffice to handle all data in a one-shot experiment (e.g. in the case of huge data bases or web applications). Data is therefore segmented and processed sequentially and incrementally in an online way. In pure online applications, individual data samples arrive over time requiring again incremental processing. This special issue intends to draw a picture of the recent advances in fuzzy online learning as a bridge between online ML and DM on one side and fuzzy theory on the other side.

Period	15 Jan 2013