[1]
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The online performance estimation framework: heterogeneous ensemble
learning for data streams
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, and Joaquin Vanschoren.
The online performance estimation framework: heterogeneous ensemble
learning for data streams.
Machine Learning, 107(1):149-176, 2018.
[ bib |
http ]
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
online.
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[2]
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SemEval-2018 Task 1: Affect in Tweets
Saif M. Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana
Kiritchenko.
Semeval-2018 Task 1: Affect in tweets.
In Proceedings of International Workshop on Semantic Evaluation
(SemEval-2018), New Orleans, LA, USA, 2018.
[ bib |
.pdf ]
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.
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