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Generating Datasets for Classification Task and Predicting Best Classifiers with Conditional Generative Adversarial Networks

Published: 21 January 2020 Publication History

Abstract

We focus on the algorithm selection problem and closely related dataset synthesis problem. We present conditional deep convolutional generative adversarial network we call LM-GAN, generator of which is capable of synthesizing dataset for classification in the matrix form with numeric features and discriminator of which can perform the best classifier prediction for a new never seen dataset. We also suggest a technique for transforming matrices representing datasets to a canonical form. Experimental evaluation shows that the presented network working with matrices in the canonical form outperforms baseline solutions in dataset synthesis and the best classifier prediction.

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Cited By

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  • (2020)Graph Convolutional Network Based Generative Adversarial Networks for the Algorithm Selection Problem in ClassificationProceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System10.1145/3437802.3437818(88-92)Online publication date: 27-Oct-2020
  • (2020)Stabilization of Dataset Matrix Form for Classification Dataset Generation and Algorithm SelectionIntelligent Data Engineering and Automated Learning – IDEAL 202010.1007/978-3-030-62365-4_7(66-75)Online publication date: 27-Oct-2020

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  1. Generating Datasets for Classification Task and Predicting Best Classifiers with Conditional Generative Adversarial Networks

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        cover image ACM Other conferences
        ICAAI '19: Proceedings of the 3rd International Conference on Advances in Artificial Intelligence
        October 2019
        253 pages
        ISBN:9781450372534
        DOI:10.1145/3369114
        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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        • Northumbria University: University of Northumbria at Newcastle

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        Published: 21 January 2020

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        Author Tags

        1. Machine Learning
        2. Meta-Learning
        3. Neural Networks

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        • (2020)Graph Convolutional Network Based Generative Adversarial Networks for the Algorithm Selection Problem in ClassificationProceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System10.1145/3437802.3437818(88-92)Online publication date: 27-Oct-2020
        • (2020)Stabilization of Dataset Matrix Form for Classification Dataset Generation and Algorithm SelectionIntelligent Data Engineering and Automated Learning – IDEAL 202010.1007/978-3-030-62365-4_7(66-75)Online publication date: 27-Oct-2020

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