research-article

PGGP

Authors:

Hugo Jair Escalante,

Alicia Morales-ReyesAuthors Info & Claims

Applied Soft Computing, Volume 40, Issue C

Pages 569 - 580

https://doi.org/10.1016/j.asoc.2015.12.015

Published: 01 March 2016 Publication History

Abstract

Graphical abstractDisplay Omitted HighlightsA genetic program for prototype generation (PGGP) is proposed.PGGP learns to combine instances via genetic programming to generate prototypes.An extensive experimental evaluation is performed.The proposed method is compared to many other techniques.The proposed approach compares favorably with most generation methods proposed so far. Prototype generation (PG) methods aim to find a subset of instances taken from a large training data set, in such a way that classification performance (commonly, using a 1NN classifier) when using prototypes is equal or better than that obtained when using the original training set. Several PG methods have been proposed so far, most of them consider a small subset of training instances as initial prototypes and modify them trying to maximize the classification performance on the whole training set. Although some of these methods have obtained acceptable results, training instances may be under-exploited, because most of the times they are only used to guide the search process. This paper introduces a PG method based on genetic programming in which many training samples are combined through arithmetic operators to build highly effective prototypes. The genetic program aims to generate prototypes that maximize an estimate of the generalization performance of an 1NN classifier. Experimental results are reported on benchmark data to assess PG methods. Several aspects of the genetic program are evaluated and compared to many alternative PG methods. The empirical assessment shows the effectiveness of the proposed approach outperforming most of the state of the art PG techniques when using both small and large data sets. Better results were obtained for data sets with numeric attributes only, although the performance of the proposed technique on mixed data was very competitive as well.

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Cited By

Ougiaroglou SPapadimitriou NEvangelidis G(2024)Reduction Through Homogeneous Clustering: Variations for Categorical Data and Fast Data ReductionSN Computer Science10.1007/s42979-024-03007-95:6Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1007/s42979-024-03007-9
Ougiaroglou SEvangelidis G(2023)Very fast variations of training set size reduction algorithms for instance-based classificationProceedings of the 27th International Database Engineered Applications Symposium10.1145/3589462.3589493(64-70)Online publication date: 5-May-2023
https://dl.acm.org/doi/10.1145/3589462.3589493
Valero-Mas JGallego AAlonso-Jiménez PSerra X(2023)Multilabel Prototype Generation for data reduction in K-Nearest Neighbour classificationPattern Recognition10.1016/j.patcog.2022.109190135:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.patcog.2022.109190
Show More Cited By

PGGP
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 40, Issue C

March 2016

683 pages

ISSN:1568-4946

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 March 2016

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Cited By

Ougiaroglou SPapadimitriou NEvangelidis G(2024)Reduction Through Homogeneous Clustering: Variations for Categorical Data and Fast Data ReductionSN Computer Science10.1007/s42979-024-03007-95:6Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1007/s42979-024-03007-9
Ougiaroglou SEvangelidis G(2023)Very fast variations of training set size reduction algorithms for instance-based classificationProceedings of the 27th International Database Engineered Applications Symposium10.1145/3589462.3589493(64-70)Online publication date: 5-May-2023
https://dl.acm.org/doi/10.1145/3589462.3589493
Valero-Mas JGallego AAlonso-Jiménez PSerra X(2023)Multilabel Prototype Generation for data reduction in K-Nearest Neighbour classificationPattern Recognition10.1016/j.patcog.2022.109190135:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.patcog.2022.109190
Liu FYan Y(2023)A Constructive Method for Data Reduction and Imbalanced SamplingAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0798-0_28(476-489)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1007/978-981-97-0798-0_28
Penarrubia CValero-Mas JGallego ACalvo-Zaragoza J(2023)Addressing Class Imbalance in Multilabel Prototype Generation for k-Nearest Neighbor ClassificationPattern Recognition and Image Analysis10.1007/978-3-031-36616-1_2(15-27)Online publication date: 27-Jun-2023
https://dl.acm.org/doi/10.1007/978-3-031-36616-1_2
Giorginis TOugiaroglou SEvangelidis GDervos D(2022)Fast data reduction by space partitioning via convex hull and MBR computationPattern Recognition10.1016/j.patcog.2022.108553126:COnline publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1016/j.patcog.2022.108553
Silva Lde Vasconcelos BDel-Moral-Hernandez E(2021)A model to estimate the Self-Organizing Maps grid dimension for Prototype GenerationIntelligent Data Analysis10.3233/IDA-20512325:2(321-338)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/IDA-205123
Shen YZheng KWu CYang YLi F(2019)A Neighbor Prototype Selection Method Based on CCHPSO for Intrusion DetectionSecurity and Communication Networks10.1155/2019/12834952019Online publication date: 20-Aug-2019
https://dl.acm.org/doi/10.1155/2019/1283495
Valencia-Ramírez JGraff MEscalante HCerda-Jacobo J(2017)An iterative genetic programming approach to prototype generationGenetic Programming and Evolvable Machines10.1007/s10710-016-9279-318:2(123-147)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10710-016-9279-3

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