skip to main content
10.1145/3437802.3437818acmotherconferencesArticle/Chapter ViewAbstractPublication PagesccrisConference Proceedingsconference-collections
research-article

Graph Convolutional Network Based Generative Adversarial Networks for the Algorithm Selection Problem in Classification

Published: 04 January 2021 Publication History

Abstract

In this work, we address the algorithm selection problem for classification via meta-learning and generative adversarial networks. We focus on the dataset representation question. The matrix representation of classification dataset is not sensitive to swapping any two rows or any two columns. We suggest a special method to reduce a dataset to a unified form. This allows to apply generative adversarial networks to classification dataset generation. In this setting, a generator generates new classification datasets in a matrix form, while a conditional discriminator is trying to predict for a dataset and an algorithm if the dataset is real and the algorithm would show the best performance on this dataset. We also suggest a graph convolutional network as a discriminator that is capable to work with such forms, which encode a dataset as a weighted graph with nodes representing objects.

References

[1]
Ashvini Balte, Nitin Pise, and Parag Kulkarni. 2014. Meta-learning with landmarking: A survey. International Journal of Computer Applications 105, 8(2014).
[2]
Pavel Brazdil, Christophe Giraud Carrier, Carlos Soares, and Ricardo Vilalta. 2008. Metalearning: Applications to data mining. Springer Science & Business Media.
[3]
Matthias Fey and Jan Eric Lenssen. 2019. Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428(2019).
[4]
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672–2680.
[5]
Kazuyuki Hara, Daisuke Saito, and Hayaru Shouno. 2015. Analysis of function of rectified linear unit used in deep learning. In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 1–8.
[6]
Ilya Kachalsky, Alexey Zabashta, Andrey Filchenkov, and Georgiy Korneev. 2019. Generating Datasets for Classification Task and Predicting Best Classifiers with Conditional Generative Adversarial Networks. In Proceedings of the 2019 3rd International Conference on Advances in Artificial Intelligence. 97–101.
[7]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).
[8]
Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016).
[9]
Vipin Kumar. 1992. Algorithms for constraint-satisfaction problems: A survey. AI magazine 13, 1 (1992), 32–32.
[10]
Steve Lawrence, C Lee Giles, Ah Chung Tsoi, and Andrew D Back. 1997. Face recognition: A convolutional neural-network approach. IEEE transactions on neural networks 8, 1 (1997), 98–113.
[11]
Christiane Lemke, Marcin Budka, and Bogdan Gabrys. 2015. Metalearning: a survey of trends and technologies. Artificial intelligence review 44, 1 (2015), 117–130.
[12]
Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, 2018. Multivariate time series imputation with generative adversarial networks. In Advances in Neural Information Processing Systems. 1596–1607.
[13]
Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784(2014).
[14]
Nicholas Nethercote, Peter J Stuckey, Ralph Becket, Sebastian Brand, Gregory J Duck, and Guido Tack. 2007. MiniZinc: Towards a standard CP modelling language. In International Conference on Principles and Practice of Constraint Programming. Springer, 529–543.
[15]
Michela Paganini, Luke de Oliveira, and Benjamin Nachman. 2018. Accelerating science with generative adversarial networks: an application to 3D particle showers in multilayer calorimeters. Physical review letters 120, 4 (2018), 042003.
[16]
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, 2019. Pytorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems. 8026–8037.
[17]
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12 (2011), 2825–2830.
[18]
Yuki Saito, Shinnosuke Takamichi, and Hiroshi Saruwatari. 2017. Statistical parametric speech synthesis incorporating generative adversarial networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing 26, 1(2017), 84–96.
[19]
C Saranya and G Manikandan. 2013. A study on normalization techniques for privacy preserving data mining. International Journal of Engineering and Technology (IJET) 5, 3(2013), 2701–2704.
[20]
DM Tax and RP Duin. 2000. Feature scaling in support vector data descriptions. Learning from Imbalanced Datasets(2000), 25–30.
[21]
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo. 2013. OpenML: Networked Science in Machine Learning. SIGKDD Explorations 15, 2 (2013), 49–60. https://doi.org/10.1145/2641190.2641198
[22]
Ricardo Vilalta, Christophe Giraud-Carrier, and Pavel Brazdil. 2009. Meta-learning-concepts and techniques. In Data mining and knowledge discovery handbook. Springer, 717–731.
[23]
David H Wolpert. 2002. The supervised learning no-free-lunch theorems. In Soft computing and industry. Springer, 25–42.
[24]
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. 2019. Modeling tabular data using conditional gan. In Advances in Neural Information Processing Systems. 7335–7345.
[25]
Matthew D Zeiler, Dilip Krishnan, Graham W Taylor, and Rob Fergus. 2010. Deconvolutional networks. In 2010 IEEE Computer Society Conference on computer vision and pattern recognition. IEEE, 2528–2535.
[26]
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas. 2017. Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In Proceedings of the IEEE international conference on computer vision. 5907–5915.
[27]
Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2020. Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering (2020).

Cited By

View all
  • (2022)A Decision-Making Tool for Algorithm Selection Based on a Fuzzy TOPSIS Approach to Solve Replenishment, Production and Distribution Planning ProblemsMathematics10.3390/math1009154410:9(1544)Online publication date: 4-May-2022
  1. Graph Convolutional Network Based Generative Adversarial Networks for the Algorithm Selection Problem in Classification

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    CCRIS '20: Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System
    October 2020
    217 pages
    ISBN:9781450388054
    DOI:10.1145/3437802
    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]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 04 January 2021

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Algorithm selection
    2. Dataset representation
    3. Dataset synthesis
    4. Generative adversarial nets
    5. Graph convolutional network
    6. Machine learning
    7. Meta-learning

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    Conference

    CCRIS 2020

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)4
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 20 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)A Decision-Making Tool for Algorithm Selection Based on a Fuzzy TOPSIS Approach to Solve Replenishment, Production and Distribution Planning ProblemsMathematics10.3390/math1009154410:9(1544)Online publication date: 4-May-2022

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media