Browsing by Author "Holmes, Geoffrey"

Now showing items 11-15 of 105

  • Case study on bagging stable classifiers for data streams

    van Rijn, Jan N.; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin (2015)
    Ensembles of classifiers are among the strongest classi-fiers in most data mining applications. Bagging ensembles exploit the instability of base-classifiers by training them on different bootstrap replicates. It has been ...

  • Classifier chains for multi-label classification

    Read, Jesse; Pfahringer, Bernhard; Holmes, Geoffrey; Frank, Eibe (Springer, 2009)
    The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence ...

  • Classifier chains: A review and perspectives

    Read, Jesse; Pfahringer, Bernhard; Holmes, Geoffrey; Frank, Eibe (AI Access Foundation, 2021)
    The family of methods collectively known as classifier chains has become a popular approach to multi-label learning problems. This approach involves chaining together off-the-shelf binary classifiers in a directed structure, ...

  • Clustering for classification

    Evans, Reuben James Emmanuel; Pfahringer, Bernhard; Holmes, Geoffrey (IEEE, 2011)
    Advances in technology have provided industry with an array of devices for collecting data. The frequency and scale of data collection means that there are now many large datasets being generated. To find patterns in these ...

  • Clustering large datasets using cobweb and K-means in tandem

    Li, Mi; Holmes, Geoffrey; Pfahringer, Bernhard (Springer, Berlin, 2005)
    This paper presents a single scan algorithm for clustering large datasets based on a two phase process which combines two well known clustering methods. The Cobweb algorithm is modified to produce a balanced tree with ...