4.6 Review

Machine learning methods for generating high dimensional discrete datasets

出版社

WILEY PERIODICALS, INC
DOI: 10.1002/widm.1450

关键词

constraints-based models; data generation; generative adversarial networks; generative models; inverse frequent itemset mining; synthetic dataset; variational autoencoder

资金

  1. European Commission [952026]
  2. Ministero dell'Istruzione, dell'Universita e della Ricerca [ARS01_00587]
  3. National Science Foundation [1820685]
  4. Direct For Education and Human Resources
  5. Division Of Graduate Education [1820685] Funding Source: National Science Foundation

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The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real-life datasets. This survey explores two possible approaches for synthesizing datasets that reflect patterns of real ones, and compares their pros and cons.
The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real-life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two-step approach: first, a real dataset X is analyzed to derive relevant patterns Z and, then, to use such patterns for reconstructing a new dataset X ' that preserves the main characteristics of X. This survey explores two possible approaches: (1) Constraint-based generation and (2) probabilistic generative modeling. The former is devised using inverse mining (IFM) techniques, and consists of generating a dataset satisfying given support constraints on the itemsets of an input set, that are typically the frequent ones. By contrast, for the latter approach, recent developments in probabilistic generative modeling (PGM) are explored that model the generation as a sampling process from a parametric distribution, typically encoded as neural network. The two approaches are compared by providing an overview of their instantiations for the case of discrete data and discussing their pros and cons. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Machine Learning Algorithmic Development > Structure Discovery

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