Combining Parameter Space Search and Meta-learning for Data-Dependent Computational Agent Recommendation

Abstract

The goal of our data-mining multi-agent system is to facilitate data-mining experiments without the necessary knowledge of the most suitable machine learning method and its parameters to the data. In order to replace the expert's knowledge, the meta-learning subsystems are proposed including the parameter-space search and method recommendation based on previous experiments.

In this paper we show the results of the parameter-space search with several search algorithms - tabulation, random search, simmulated annealing, and genetic algorithm.

Keywords

• Computational modeling
• Evolutionary computation
• Linear programming
• Mean square error methods
• Optimization
• Support vector machines
• Training
• Multiobjective optimization
• meta-learning
• model selection
• surrogate modelling