AutoUniv

Raymond Hickey

    Research output: Non-textual formSoftware

    Abstract

    AutoUniv (AU) is a tool for creating classification models and generating data sets from them. AU is freely available for use by the Machine Learning and Data Mining communities in developing and testing algorithms which learn to classify.
    LanguageEnglish
    Publication statusPublished - 15 Sep 2010

    Fingerprint

    Data mining
    Learning systems
    Testing

    Keywords

    • Classification models
    • Machine Learning
    • Data Mining
    • artificial data generation
    • algorithm evaluation.

    Cite this

    Hickey, R. (Author). (2010). AutoUniv. Software, Retrieved from http://sites.google.com/site/autouniv
    Hickey, Raymond (Author). / AutoUniv. [Software].
    @misc{3a499a4e743d4cbebb812a2b3ee5fb56,
    title = "AutoUniv",
    abstract = "AutoUniv (AU) is a tool for creating classification models and generating data sets from them. AU is freely available for use by the Machine Learning and Data Mining communities in developing and testing algorithms which learn to classify.",
    keywords = "Classification models, Machine Learning, Data Mining, artificial data generation, algorithm evaluation.",
    author = "Raymond Hickey",
    note = "Reference text: [1] UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/ . [2] LED Display Domain Data Set. http://archive.ics.uci.edu/ml/datasets/LED+Display+Domain . [3] Hickey RJ (1996) Noise modelling and evaluating learning from examples, Artificial Intelligence 82 : 157–179. [4] Black M and Hickey RJ (1999) Maintaining the performance of a learned classifier under concept drift, Intelligent Data Analysis 3 : 453-474. [5] Hickey RJ (2003) Learning Rare Class Footprints: the REFLEX Algorithm, Proceedings of Proceedings of the Second International Workshop on Learning with Imbalanced Data Sets, International Conference on Machine Learning, Washington, D.C., 21-24 August 2003 :89-96. Outputmediatype: Available for download on web",
    year = "2010",
    month = "9",
    day = "15",
    language = "English",

    }

    Hickey, R, AutoUniv, 2010, Software.
    AutoUniv. Hickey, Raymond (Author). 2010.

    Research output: Non-textual formSoftware

    TY - ADVS

    T1 - AutoUniv

    AU - Hickey, Raymond

    N1 - Reference text: [1] UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/ . [2] LED Display Domain Data Set. http://archive.ics.uci.edu/ml/datasets/LED+Display+Domain . [3] Hickey RJ (1996) Noise modelling and evaluating learning from examples, Artificial Intelligence 82 : 157–179. [4] Black M and Hickey RJ (1999) Maintaining the performance of a learned classifier under concept drift, Intelligent Data Analysis 3 : 453-474. [5] Hickey RJ (2003) Learning Rare Class Footprints: the REFLEX Algorithm, Proceedings of Proceedings of the Second International Workshop on Learning with Imbalanced Data Sets, International Conference on Machine Learning, Washington, D.C., 21-24 August 2003 :89-96. Outputmediatype: Available for download on web

    PY - 2010/9/15

    Y1 - 2010/9/15

    N2 - AutoUniv (AU) is a tool for creating classification models and generating data sets from them. AU is freely available for use by the Machine Learning and Data Mining communities in developing and testing algorithms which learn to classify.

    AB - AutoUniv (AU) is a tool for creating classification models and generating data sets from them. AU is freely available for use by the Machine Learning and Data Mining communities in developing and testing algorithms which learn to classify.

    KW - Classification models

    KW - Machine Learning

    KW - Data Mining

    KW - artificial data generation

    KW - algorithm evaluation.

    M3 - Software

    ER -

    Hickey R (Author). AutoUniv 2010.