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Title: Fast Training Set Size Reduction Using Simple Space Partitioning Algorithms
Authors: Ougiaroglou, Stefanos
Mastromanolis, Theodoros
Evangelidis, Georgios
Margaris, Dionisis
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: data reduction
Reduction by Space Partitioning
prototype generation
instance-based classification
kNN classifier
Issue Date: 10-Dec-2022
Publisher: Multidisciplinary Digital Publishing Institute
Source: Information
Volume: 13
Issue: 12
First Page: 572
Abstract: The Reduction by Space Partitioning (RSP3) algorithm is a well-known data reduction technique. It summarizes the training data and generates representative prototypes. Its goal is to reduce the computational cost of an instance-based classifier without penalty in accuracy. The algorithm keeps on dividing the initial training data into subsets until all of them become homogeneous, i.e., they contain instances of the same class. To divide a non-homogeneous subset, the algorithm computes its two furthest instances and assigns all instances to their closest furthest instance. This is a very expensive computational task, since all distances among the instances of a non-homogeneous subset must be calculated. Moreover, noise in the training data leads to a large number of small homogeneous subsets, many of which have only one instance. These instances are probably noise, but the algorithm mistakenly generates prototypes for these subsets. This paper proposes simple and fast variations of RSP3 that avoid the computationally costly partitioning tasks and remove the noisy training instances. The experimental study conducted on sixteen datasets and the corresponding statistical tests show that the proposed variations of the algorithm are much faster and achieve higher reduction rates than the conventional RSP3 without negatively affecting the accuracy.
ISSN: 2078-2489
Other Identifiers: 10.3390/info13120572
Appears in Collections:Department of Applied Informatics

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