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      Online estimation of discrete, continuous, and conditional joint densities using classifier chains

      Geilke, Michael; Karwath, Andreas; Frank, Eibe; Kramer, Stefan
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      Data Min Knowl Disc paper.pdf
      Published version, 1.151Mb
      DOI
       10.1007/s10618-017-0546-6
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      Geilke, M., Karwath, A., Frank, E., & Kramer, S. (2018). Online estimation of discrete, continuous, and conditional joint densities using classifier chains. Data Mining and Knowledge Discovery, 32(3), 561–603. https://doi.org/10.1007/s10618-017-0546-6
      Permanent Research Commons link: https://hdl.handle.net/10289/12031
      Abstract
      We address the problem of estimating discrete, continuous, and conditional joint densities online, i.e., the algorithm is only provided the current example and its current estimate for its update. The family of proposed online density estimators, estimation of densities online (EDO), uses classifier chains to model dependencies among features, where each classifier in the chain estimates the probability of one particular feature. Because a single chain may not provide a reliable estimate, we also consider ensembles of classifier chains and ensembles of weighted classifier chains. For all density estimators, we provide consistency proofs and propose algorithms to perform certain inference tasks. The empirical evaluation of the estimators is conducted in several experiments and on datasets of up to several millions of instances. In the discrete case, we compare our estimators to density estimates computed by Bayesian structure learners. In the continuous case, we compare them to a state-of-the-art online density estimator. Our experiments demonstrate that, even though designed to work online, EDO delivers estimators of competitive accuracy compared to other density estimators (batch Bayesian structure learners on discrete datasets and the state-of-the-art online density estimator on continuous datasets). Besides achieving similar performance in these cases, EDO is also able to estimate densities with mixed types of variables, i.e., discrete and continuous random variables.
      Date
      2018
      Type
      Journal Article
      Publisher
      Springer
      Rights
      © The Author(s) 2017
      Collections
      • Computing and Mathematical Sciences Papers [1455]
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