@COMMENT{{Automatically generated - DO NOT MODIFY!}}

  AUTHOR = {Jan N. van Rijn and
               Geoffrey Holmes and
               Bernhard Pfahringer and
               Joaquin Vanschoren},
  TITLE = {The online performance estimation framework: heterogeneous ensemble
               learning for data streams},
  JOURNAL = {Machine Learning},
  VOLUME = {107},
  NUMBER = {1},
  PAGES = {149--176},
  YEAR = {2018},
  URL = {https://doi.org/10.1007/s10994-017-5686-9},
  ABSTRACT = {Ensembles of classifiers are among the best performing classifiers available in
  many data mining applications, including the mining of data streams. Rather than training
  one classifier, multiple classifiers are trained, and their predictions are combined according
  to a given voting schedule. An important prerequisite for ensembles to be successful is that
  the individual models are diverse. One way to vastly increase the diversity among the models
  is to build an heterogeneous ensemble, comprised of fundamentally different model types.
  However, most ensembles developed specifically for the dynamic data stream setting rely on
  only one type of base-level classifier, most often Hoeffding Trees. We study the use of
  heterogeneous ensembles for data streams.We introduce the Online Performance Estimation
  framework, which dynamically weights the votes of individual classifiers in an ensemble.
  Using an internal evaluation on recent training data, it measures how well ensemblemembers
  performed on this and dynamically updates their weights. Experiments over a wide range of
  data streams show performance that is competitive with state of the art ensemble techniques,
  including Online Bagging and Leveraging Bagging, while being significantly
  faster. All experimental results from this work are easily reproducible and publicly available

  AUTHOR = {Mohammad, Saif M. and  Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
  TITLE = {SemEval-2018 {T}ask 1: {A}ffect in Tweets},
  BOOKTITLE = {Proceedings of International Workshop on Semantic Evaluation (SemEval-2018)},
  ADDRESS = {New Orleans, LA, USA},
  YEAR = {2018},
  PDF = {https://www.cs.waikato.ac.nz/ml/publications/2018/semeval2018.pdf},
  ABSTRACT = {We present the SemEval-2018 Task 1: Affect
  in Tweets, which includes an array of subtasks
  on inferring the affectual state of a person from
  their tweet. For each task, we created labeled
  data from English, Arabic, and Spanish tweets.
  The individual tasks are: 1. emotion intensity
  regression, 2. emotion intensity ordinal classification,
  3. valence (sentiment) regression, 4.
  valence ordinal classification, and 5. emotion
  classification. Seventy-five teams (about 200
  team members) participated in the shared task.
  We summarize the methods, resources, and
  tools used by the participating teams, with a
  focus on the techniques and resources that are
  particularly useful. We also analyze systems
  for consistent bias towards a particular race or
  gender. The data is made freely available to
  further improve our understanding of how people
  convey emotions through language.}