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LED显示域数据集

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Computer Classification

Original Source:Breiman,L., Friedman,J.H., Olshen,R.A.,Stone,C.J. (1984). Classification and Regression Trees. Wadsworth......

数据结构 ? 5K

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Original Source:

    Breiman,L., Friedman,J.H., Olshen,R.A., & Stone,C.J. (1984).  
    Classification and Regression Trees.  Wadsworth International Group: Belmont, California.  (see pages 43-49).

    Donor:

    David Aha


    Data Set Information:

    This simple domain contains 7 Boolean attributes and 10 concepts, the set of decimal digits.  Recall that LED displays contain 7 light-emitting diodes -- hence the reason for 7 attributes.  The problem would be easy if not for the introduction of noise.  In this case, each attribute value has the 10% probability of having its value inverted.  

    It's valuable to know the optimal Bayes rate for these databases. In this case, the misclassification rate is 26% (74% classification accuracy).


    Attribute Information:

    -- All attribute values are either 0 or 1, according to whether the corresponding light is on or not for the decimal digit.
      -- Each attribute (excluding the class attribute, which is an integer ranging between 0 and 9 inclusive) has a 10% percent chance of being inverted.


    Relevant Papers:

    Breiman,L., Friedman,J.H., Olshen,R.A., & Stone,C.J.  Classification and Regression Trees.  Wadsworth International Group: Belmont, California. 1984. (see pages 43-49).
    [Web link]

    Quinlan,J.R. (1987). Simplifying Decision Trees.  In International Journal of Man-Machine Studies.
    [Web link]

    Tan,M. & Eshelman,L. (1988). Using Weighted Networks to Represent Classification Knowledge in Noisy Domains.  In Proceedings of the 5th International Conference on Machine Learning, 121-134, Ann Arbor, Michigan: Morgan Kaufmann.  
    [Web link]


    Papers That Cite This Data Set1:


    Joao Gama and Ricardo Rocha and Pedro Medas. Accurate decision trees for mining high-speed data streams. KDD. 2003.  [View Context].

    Tim Leunig and D. Stott Parker. Empirical comparisons of various voting methods in bagging. KDD. 2003.  [View Context].

    Xavier Llor and David E. Goldberg and Ivan Traus and Ester Bernad i Mansilla. Accuracy, Parsimony, and Generality in Evolutionary Learning Systems via Multiobjective Selection. IWLCS. 2002.  [View Context].

    Xavier Llor and David E. Goldberg. Minimal Achievable Error in the LED. Illinois Genetic Algorithms Laboratory University of Illinois at Urbana-Champaign. 2002.  [View Context].

    Huan Liu and Rudy Setiono. Incremental Feature Selection. Appl. Intell, 9. 1998.  [View Context].

    Kamal Ali and Michael J. Pazzani. Error Reduction through Learning Multiple Descriptions. Machine Learning, 24. 1996.  [View Context].

    Ramon Sangesa and Ulises Cortes. Possibilistic Conditional Dependency, Similarity and Information Measures: an application to causal network recovery. Departament de Llenguatges i Sistemes Informtics Departament de Llenguatges i Sistemes Informtics Technical University of Catalonia Technical University of Catalonia.  [View Context].

    Vikas Sindhwani and P. Bhattacharya and Subrata Rakshit. Information Theoretic Feature Crediting in Multiclass Support Vector Machines.  [View Context].

    Maria Salamo and Elisabet

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