Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/1199
Title: | EHC: Non-parametric Editing by Finding Homogeneous Clusters |
Authors: | Ougiaroglou, Stefanos Evangelidis, Georgios |
Type: | Conference Paper |
Subjects: | FRASCATI::Natural sciences::Computer and information sciences |
Keywords: | k-NN classification clustering editing noisy items |
Issue Date: | 2014 |
Volume: | 8367 |
First Page: | 290 |
Last Page: | 304 |
Volume Title: | Foundations of Information and Knowledge Systems |
Part of Series: | Lecture Notes in Computer Science |
Part of Series: | Lecture Notes in Computer Science |
Abstract: | Editing is a crucial data mining task in the context of k-Nearest Neighbor classification. Its purpose is to improve classification accuracy by improving the quality of training datasets. To obtain such datasets, editing algorithms try to remove noisy and mislabeled data as well as smooth the decision boundaries between the discrete classes. In this paper, a new fast and non-parametric editing algorithm is proposed. It is called Editing through Homogeneous Clusters (EHC) and is based on an iterative execution of a clustering procedure that forms clusters containing items of a specific class only. Contrary to other editing approaches, EHC is independent of input (tuning) parameters. The performance of EHC is experimentally compared to three state-of-the-art editing algorithms on ten datasets. The results show that EHC is faster than its competitors and achieves high classification accuracy. |
URI: | https://doi.org/10.1007/978-3-319-04939-7_14 https://ruomo.lib.uom.gr/handle/7000/1199 |
ISBN: | 978-3-319-04938-0 978-3-319-04939-7 |
ISSN: | 0302-9743 1611-3349 |
Other Identifiers: | 10.1007/978-3-319-04939-7_14 |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
File | Description | Size | Format | |
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2014_FOIKS.pdf | 163,82 kB | Adobe PDF | View/Open |
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