<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Meta-learning is becoming more and more important in current and future research concentrated around broadly defined data mining or computational intelligence. It can solve problems that cannot be solved by any single, specialized algorithm. The overall characteristic of each meta-learning algorithm mainly depends on two elements: the learning machine space and the supervisory procedure. The former restricts the space of all possible learning machines to a subspace to be browsed by a meta-learning algorithm. The latter determines the order of selected learning machines with a module responsible for machine complexity evaluation, organizes tests and performs analysis of results. In this article we present a framework for meta-learning search that can be seen as a method of sophisticated description and evaluation of functional search spaces of learning machine configurations used in meta-learning. Machine spaces will be defined by specially defined graphs where vertices are specialized machine configuration generators. By using such graphs the learning machine space may be modeled in a much more flexible way, depending on the characteristics of the problem considered and a priori knowledge. The presented method of search space description is used together with an advanced algorithm which orders test tasks according to their complexities.</p></abstract>

Meta-learning is becoming more and more important in current and future research concentrated around broadly defined data mining or computational intelligence. It can solve problems that cannot be solved by any single, specialized algorithm. The overall characteristic of each meta-learning algorithm mainly depends on two elements: the learning machine space and the supervisory procedure. The former restricts the space of all possible learning machines to a subspace to be browsed by a meta-learning algorithm. The latter determines the order of selected learning machines with a module responsible for machine complexity evaluation, organizes tests and performs analysis of results. In this article we present a framework for meta-learning search that can be seen as a method of sophisticated description and evaluation of functional search spaces of learning machine configurations used in meta-learning. Machine spaces will be defined by specially defined graphs where vertices are specialized machine configuration generators. By using such graphs the learning machine space may be modeled in a much more flexible way, depending on the characteristics of the problem considered and a priori knowledge. The presented method of search space description is used together with an advanced algorithm which orders test tasks according to their complexities.

Graph-based generation of a meta-learning search space

Department of Informatics Nicolaus Copernicus University, ul. Grudzia˛dzka 5, 87-100 Toruń, Poland

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Graph-based generation of a meta-learning search space"}

Meta-learning is becoming more and more important in current and future research concentrated around broadly defined data mining or computational intelligence....

Graph-based generation of a meta-learning search space

Online veröffentlicht: 28. Sept. 2012

Seitenbereich: 647 - 667

DOI: https://doi.org/10.2478/v10006-012-0049-y

Schlüsselwörter
meta-learning, data mining, learning machines, complexity of learning, complexity of learning machines, computational intelligence

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Graph-based generation of a meta-learning search space

Online veröffentlicht: 28. Sept. 2012

Seitenbereich: 647 - 667

DOI: https://doi.org/10.2478/v10006-012-0049-y

Schlüsselwörtermeta-learning, data mining, learning machines, complexity of learning, complexity of learning machines, computational intelligence

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
meta-learning, data mining, learning machines, complexity of learning, complexity of learning machines, computational intelligence