Browsing by Author "Pfahringer, Bernhard"

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  • Adaptive random forests for evolving data stream classification

    Gomes, Heitor Murilo; Bifet, Albert; Read, Jesse; Barddal, Jean Paul; Enembreck, Fabrício; Pfahringer, Bernhard; Holmes, Geoffrey; Abdessalem, Talel (Springer, 2017)
    Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input ...
  • Adaptive XGBoost for evolving data streams

    Montiel, Jacob; Mitchell, Rory; Frank, Eibe; Pfahringer, Bernhard; Abdessalem, Talel; Bifet, Albert (IEEE, 2020)
    Boosting is an ensemble method that combines base models in a sequential manner to achieve high predictive accuracy. A popular learning algorithm based on this ensemble method is eXtreme Gradient Boosting (XGB). We present ...
  • AffectiveTweets: a Weka package for analyzing affect in tweets

    Bravo-Marquez, Felipe; Frank, Eibe; Pfahringer, Bernhard; Mohammad, Saif M. (Microtome Publishing, 2019)
    AffectiveTweets is a set of programs for analyzing emotion and sentiment of social media messages such as tweets. It is implemented as a package for the Weka machine learning workbench and provides methods for calculating ...
  • Algorithm selection on data streams

    van Rijn, Jan N.; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin (Springer International Publishing, 2014)
    We explore the possibilities of meta-learning on data streams, in particular algorithm selection. In a first experiment we calculate the characteristics of a small sample of a data stream, and try to predict which classifier ...
  • Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis

    Bravo-Marquez, Felipe; Frank, Eibe; Pfahringer, Bernhard (IOS Press, 2016-01-01)
    The classification of tweets into polarity classes is a popular task in sentiment analysis. State-of-the-art solutions to this problem are based on supervised machine learning models trained from manually annotated examples. ...

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  • Domain-specific language models for multi-label classification of medical text

    Yogarajan, Vithya (The University of Waikato, 2022)
    Recent advancements in machine learning-based medical text multi-label classifications can be used to enhance the understanding of the human body and aid the need for patient care. This research considers predicting medical ...
  • A study of self-training variants for semi-supervised image classification

    Sahito, Attaullah (The University of Waikato, 2021)
    Artificial neural networks achieve state-of-the-art performance when trained on a vast number of labelled examples. Still, they can easily overfit training examples when few labelled examples are available. The requirement ...
  • Contextualised approaches to embedding word senses

    Ansell, Alan John (The University of Waikato, 2020)
    Vector representations of text are an essential tool for modern Natural Language Processing (NLP), and there has been much work devoted to finding effective methods for obtaining such representations. Most previously ...
  • Learning discrete and Lipschitz representations

    Gouk, Henry (The University of Waikato, 2019)
    Learning to embed data into a low dimensional vector space that is more useful for some downstream task is one of the most common problems addressed in the representation learning literature. Conventional approaches to ...
  • Tree-structured multiclass probability estimators

    Leathart, Timothy Matthew (The University of Waikato, 2019)
    Nested dichotomies are used as a method of transforming a multiclass classification problem into a series of binary problems. A binary tree structure is constructed over the label space that recursively splits the set of ...

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