Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/1172
Title: | Fast Tree-Based Classification via Homogeneous Clustering |
Authors: | Pardis, George Diamantaras, Konstantinos I. Ougiaroglou, Stefanos Evangelidis, Georgios |
Type: | Conference Paper |
Subjects: | FRASCATI::Natural sciences::Computer and information sciences |
Keywords: | Classification k-means EM Prototype Generation |
Issue Date: | 18-Oct-2019 |
Volume: | 11871 |
First Page: | 514 |
Last Page: | 524 |
Volume Title: | Intelligent Data Engineering and Automated Learning – IDEAL 2019 |
Part of Series: | Lecture Notes in Computer Science |
Part of Series: | Lecture Notes in Computer Science |
Abstract: | Data reduction, achieved by collecting a small subset of representative prototypes from the original patterns, aims at alleviating the computational burden of training a classifier without sacrificing performance. We propose an extension of the Reduction by finding Homogeneous Clusters algorithm, which utilizes the k-means method to propose a set of homogeneous cluster centers as representative prototypes. We propose two new classifiers, which recursively produce homogeneous clusters and achieve higher performance than current homogeneous clustering methods with significant speed up. The key idea is the development of a tree data structure that holds the constructed clusters. Internal tree nodes consist of clustering models, while leaves correspond to homogeneous clusters where the corresponding class label is stored. Classification is performed by simply traversing the tree. The two algorithms differ on the clustering method used to build tree nodes: the first uses k-means while the second applies EM clustering. The proposed algorithms are evaluated on a variety datasets and compared with well-known methods. The results demonstrate very good classification performance combined with large computational savings. |
URI: | https://doi.org/10.1007/978-3-030-33607-3_55 https://ruomo.lib.uom.gr/handle/7000/1172 |
ISBN: | 978-3-030-33606-6 978-3-030-33607-3 |
ISSN: | 0302-9743 1611-3349 |
Other Identifiers: | 10.1007/978-3-030-33607-3_55 |
Appears in Collections: | Department of Applied Informatics |
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