Universal meta-learning architecture and algorithms

Abstract

There are hundreds of algorithms within data mining. Some of them are used to transform data, some to build classifiers, others for prediction, etc. Nobody knows well all these algorithms and nobody can know all the arcana of their behavior in all possible applications. How to find the best combination of transformation and final machine which solves given problem? The solution is to use configurable and efficient meta-learning to solve data mining problems. Below, a general and flexible meta-learning system is presented. It can be used to solve different problems with computational intelligence, basing on learning from data. The main ideas of our meta-learning algorithms lie in complexity controlled loop, searching for most adequate models and in using special functional specification of search spaces (the meta-learning spaces) combined with flexible way of defining the goal of meta-searching. © 2011 Springer-Verlag Berlin Heidelberg.