Automatic meta-learning system supporting selection of optimal algorithm for problem solving and calculation of optimal parameters of its functioning
| Autor: | Andrey Orlov |
|---|---|
| Jazyk: | ruština |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Известия Томского политехнического университета: Инжиниринг георесурсов, Vol 324, Iss 5 (2019) |
| Druh dokumentu: | article |
| ISSN: | 2500-1019 2413-1830 |
| Popis: | The relevance of the work is caused by necessity of increasing efficiency of automatic data mining systems based on meta-learning. The main aim of the study is to design an automatic meta-learning system supporting selection of optimal algorithm for problem solving and calculation of optimal parameters of its functioning. The methods used in the study: inductive modeling, methods of statistical analysis of results. Results: The known meta-learning systems were integrated based on produced classification features taking into account internal structure of systems. The author has stated the requirements for implementation of the automatic meta-learning system and has offered the way to build a meta-learning system satisfying all stated requirements and accumulating meta-knowledge, building meta-models on its basis, selecting optimal algorithm from a set of available ones and calculating optimal parameters of its functioning. The object-oriented architecture of a software framework for implementation of any meta-learning system presented in the systematization was developed. The efficiency of the implemented automatic meta-learning system using algorithms of group method of data handling was experimentally examined being applied to solution of problems related to the short-term time series forecasting (1428 time series from the testing set known as "M3 Competition"). |
| Databáze: | Directory of Open Access Journals |
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