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The paper focuses on using stacking and rotation-based technique to improve performance and generalization ability of the machine learning classification with data reduction. The aim of data reduction technique is decreasing the quantity of information required to learn a high quality classifiers, especially when the data are huge. The paper shows that merging both stacking and rotation-based ensemble...
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...
The aim of the paper is to propose and evaluate a hybrid approach to generate a representative training dataset of the required size. Prototype selection is understood as a selection of the representative prototypes from the original training dataset. The basic assumptions underlying the proposed method is that the prototype selection is carried out after the training dataset has been grouped into...
The paper deals with the distributed learning. Distributed learning from data is considered to be an important challenge faced by researchers and practice in the domain of the distributed data mining and distributed knowledge discovery from databases. An effective approach to learning from a geographically distributed data is to select, from the local databases, relevant local patterns, called also...
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