Gained Knowledge Exchange and Analysis for Meta-Learning

Building accurate and reliable complex machines is not trivial (but necessary in most real life problems). Typical ensembles are often unsatisfactory. Meta-learning techniques can be much more powerful in composing optimal or close to optimal solutions to given tasks. Efficient meta-learning is possible only within a versatile and flexible data mining framework providing uniform procedures for dealing with different kinds of methods and tools for thorough analysis of learning processes and their results. We propose a methodology for information exchange between machines of different abstraction levels. Inter-machine communication is based on uniform representation of gained knowledge. Implemented in a general data mining framework, it provides tools for sophisticated analysis of adaptive processes of heterogeneous machines. The resulting meta-knowledge is a brilliant information source for further meta-learning.