An Approach to Machine Classification Based on Stacked Generalization and Instance Selection

This paper focuses on the machine classification with data reduction. The aim of the data reduction techniques is decreasing the quantity of information required to learn a high quality classifiers. In this paper the data reduction is carried out by selection of relevant instances, called prototypes. To solve the machine classification problem with data reduction an agent-based population learning algorithm is proposed. The discussed approach bases on the assumption that the selection of prototypes is carried-out by a team of agents and that the prototype instances are selected from clusters of instances. The proposed procedure is called the stack generalization. It aims at improving the quality of the learning process and increasing the generalization capacity of the learning model. The paper includes the description of the approach and the discussion of the validating experiment results.