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A parallel genetic programming for single class classification

Authors:

Mohamed ElatiAuthors Info & Claims

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Pages 1579 - 1586

https://doi.org/10.1145/2464576.2466811

Published: 06 July 2013 Publication History

Abstract

In this paper, we present an algorithm based on genetic programming for single (one) class classification that uses one set containing similar patterns in training process. This type of problem is called single (one) class classification, a novel detection. The proposed algorithm was tested and compared to seven other traditional methods based on two publicly available transcriptomic and proteomic time series datasets and two public breast cancer datasets. The results show that the algorithm could find most similar patterns in the databases with rather low misclassification rates. We also applied parallel genetic programming for this algorithm and it proves that the island model can give better solutions than sequential genetic programming.

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https://doi.org/10.1109/TEVC.2023.3257230
Picek SHemberg EJakobovic DO’Reilly U(2018)One-Class Classification of Low Volume DoS Attacks with Genetic ProgrammingGenetic Programming Theory and Practice XV10.1007/978-3-319-90512-9_10(149-168)Online publication date: 6-Jul-2018
https://doi.org/10.1007/978-3-319-90512-9_10
Dhifli WDa Costa NElati M(2017)An evolutionary schema for mining skyline clusters of attributed graph data2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969559(2102-2109)Online publication date: 5-Jun-2017
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Index Terms

A parallel genetic programming for single class classification
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

July 2013

1798 pages

ISBN:9781450319645

DOI:10.1145/2464576

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

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]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 06 July 2013

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GECCO '13

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GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Pei WXue BZhang MShang LYao XZhang Q(2024)A Survey on Unbalanced Classification: How Can Evolutionary Computation Help?IEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.325723028:2(353-373)Online publication date: Apr-2024
https://doi.org/10.1109/TEVC.2023.3257230
Picek SHemberg EJakobovic DO’Reilly U(2018)One-Class Classification of Low Volume DoS Attacks with Genetic ProgrammingGenetic Programming Theory and Practice XV10.1007/978-3-319-90512-9_10(149-168)Online publication date: 6-Jul-2018
https://doi.org/10.1007/978-3-319-90512-9_10
Dhifli WDa Costa NElati M(2017)An evolutionary schema for mining skyline clusters of attributed graph data2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969559(2102-2109)Online publication date: 5-Jun-2017
https://dl.acm.org/doi/10.1109/CEC.2017.7969559
Zaloga AYakimov IDubinin P(2017) Determination of the [Pt(NH 3 ) 5 Cl]Br 3 crystal structure from X-ray powder diffraction data using multi-population genetic algorithm Powder Diffraction10.1017/S088571561700019732:S1(S110-S117)Online publication date: 28-Feb-2017
https://doi.org/10.1017/S0885715617000197
Tran BPicek SXue B(2017)Automatic Feature Construction for Network Intrusion DetectionSimulated Evolution and Learning10.1007/978-3-319-68759-9_46(569-580)Online publication date: 14-Oct-2017
https://doi.org/10.1007/978-3-319-68759-9_46
Cao VNicolau MMcDermott J(2016)A Hybrid Autoencoder and Density Estimation Model for Anomaly DetectionParallel Problem Solving from Nature – PPSN XIV10.1007/978-3-319-45823-6_67(717-726)Online publication date: 31-Aug-2016
https://doi.org/10.1007/978-3-319-45823-6_67
Karimian NTehranipoor FRahman MKelly SForte D(2015)Genetic Algorithm for hardware Trojan detection with ring oscillator network (RON)2015 IEEE International Symposium on Technologies for Homeland Security (HST)10.1109/THS.2015.7225334(1-6)Online publication date: Apr-2015
https://doi.org/10.1109/THS.2015.7225334

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