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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Optimal classification by mixed-initiative nested thresholding.

Baro Hyun, Pierre Kabamba, Anouck Girard

    IEEE Transactions on Cybernetics
    |May 13, 2014
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    Summary

    We introduce a new mixed-initiative team architecture for machine and human classifiers. This novel nested design improves classification accuracy compared to traditional single or simple nested classifier systems.

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • Traditional classification systems often struggle with complex datasets.
    • Integrating human and machine intelligence presents unique performance challenges.
    • Optimizing classifier performance under varying workloads is crucial for real-world applications.

    Purpose of the Study:

    • To propose a novel mixed-initiative team architecture for machine and human classifiers.
    • To model human classifier performance as workload-dependent and machine classifier performance as workload-independent.
    • To demonstrate the superior performance of the proposed architecture over existing methods.

    Main Methods:

    • A nested classifier architecture was designed, featuring a primary trichotomous classifier (true, false, unknown) and a secondary dichotomous classifier (true, false).

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  • The primary classifier operates with workload-independent performance, handling initial data classification.
  • The secondary classifier, with workload-dependent performance, processes data classified as 'unknown' by the primary classifier.

    • Main Results:

    • The proposed novel classifier architecture significantly outperforms single dichotomous classifiers.
    • The mixed-initiative team architecture demonstrates superior performance compared to simple nested two-classifier teams.
    • The workload-independent primary classifier and workload-dependent secondary classifier effectively manage classification tasks.

    Conclusions:

    • The novel nested architecture for mixed-initiative teams offers enhanced classification accuracy.
    • This approach effectively leverages the strengths of both machine and human classifiers.
    • The proposed model provides a robust framework for improving classification performance in complex scenarios.