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AIB2: an abstraction data reduction technique based on IB2

Authors:

Stefanos Ougiaroglou,

Georgios EvangelidisAuthors Info & Claims

BCI '13: Proceedings of the 6th Balkan Conference in Informatics

Pages 13 - 16

https://doi.org/10.1145/2490257.2490260

Published: 19 September 2013 Publication History

Abstract

Data reduction improves the efficiency of k-NN classifier on large datasets since it accelerates the classification process and reduces storage requirements for the training data. IB2 is an effective data reduction technique that selects some training items form the initial dataset and uses them as representatives (prototypes). Contrary to many other data reduction techniques, IB2 is a very fast, one-pass method that builds its reduced (condensing) set in an incremental manner. New training data can update the condensing set without the need of the "old" removed items. This paper proposes AIB2, a variation of IB2, which generates new prototypes instead of selecting them. AIB2 attempts to improve the efficiency of IB2 by positioning the prototypes in the center of the data areas they represent. The experimental study shows that AIB2 performs better than IB2.

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Cited By

Xouveroudis KOugiaroglou SEvangelidis GDervos D(2021)Prototype Selection and Generation with Minority Classes Preservation2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA52424.2021.9555514(1-8)Online publication date: 12-Jul-2021
https://doi.org/10.1109/IISA52424.2021.9555514
Ougiaroglou SKaramitopoulos LTatoglou CEvangelidis GDervos D(2015)Applying Prototype Selection and Abstraction Algorithms for Efficient Time-Series ClassificationArtificial Neural Networks10.1007/978-3-319-09903-3_16(333-348)Online publication date: 2015
https://doi.org/10.1007/978-3-319-09903-3_16

Index Terms

AIB2: an abstraction data reduction technique based on IB2
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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Information

Published In

cover image ACM Other conferences

BCI '13: Proceedings of the 6th Balkan Conference in Informatics

September 2013

293 pages

ISBN:9781450318518

DOI:10.1145/2490257

Conference Chairs:
Kostas Diamantaras
TEI of Thessaloniki
,
Georgios Evangelidis
University of Macedonia
,
Yannis Manolopoulos
Aristotle University
,
Program Chairs:
Christos Georgiadis
University of Macedonia
,
Petros Kefalas
The University of Sheffield International Faculty, CITY College
,
Demosthenes Stamatis
TEI of Thessaloniki

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

University of Macedonia
Aristotle University of Thessaloniki
The University of Sheffield: The University of Sheffield
Greek Com Soc: Greek Computer Society
SEERC: South-East European Research Centre
Alexander TEI of Thessaloniki

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 September 2013

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BCI '13

Sponsor:

The University of Sheffield
Greek Com Soc
SEERC

BCI '13: Balkan Conference in Informatics

September 19 - 21, 2013

Thessaloniki, Greece

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BCI '13 Paper Acceptance Rate 41 of 103 submissions, 40%;

Overall Acceptance Rate 97 of 250 submissions, 39%

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Cited By

Xouveroudis KOugiaroglou SEvangelidis GDervos D(2021)Prototype Selection and Generation with Minority Classes Preservation2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA52424.2021.9555514(1-8)Online publication date: 12-Jul-2021
https://doi.org/10.1109/IISA52424.2021.9555514
Ougiaroglou SKaramitopoulos LTatoglou CEvangelidis GDervos D(2015)Applying Prototype Selection and Abstraction Algorithms for Efficient Time-Series ClassificationArtificial Neural Networks10.1007/978-3-319-09903-3_16(333-348)Online publication date: 2015
https://doi.org/10.1007/978-3-319-09903-3_16

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