Application of agent-based simulated annealing and tabu search procedures to solving the data reduction problem

Abstract

The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction results in increased capabilities and generalization properties of the learning model and a shorter time of the learning process. It can also help in scaling up to large data sources. The paper proposes an agent-based data reduction approach with the learning process executed by a team of agents (A-Team). Several A-Team architectures with agents executing the simulated annealing and tabu search procedures are proposed and investigated. The paper includes a detailed description of the proposed approach and discusses the results of a validating experiment.

Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms

“…For the authors proposing novel classification and regression algorithms (Baruque et al, 2011;Czarnowski and Jędrzejowicz, 2011;Jackowski and Woźniak, 2010;Kajdanowicz and Kazienko, 2011;Troć and Unold, 2010), especially 1 × N procedures are recommended because they are simpler and require less measurement points than N × N procedures to provide meaningful output.…”

Survey of Object-Based Data Reduction Techniques in Observational Astronomy

“…As previously mentioned, the data set size can be reduced to speed up data analysis calculations or make them at all feasible [7]. For astronomical datasets it is frequently used only to enable informative visualizations.…”

JABAT Middleware as a Tool for Solving Optimization Problems

“…The existence of the synergetic effect was also shown by the experiment described in [12]. There, tasks from data reduction problem were solved with the use of different two agent teams.…”