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Pareto-Optimal Model Selection via SPRINT-Race.

Tiantian Zhang, Michael Georgiopoulos, Georgios C Anagnostopoulos

    IEEE Transactions on Cybernetics
    |February 7, 2017
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    Summary
    This summary is machine-generated.

    SPRINT-Race is a novel algorithm for multi-objective model selection (MOMS), efficiently identifying Pareto-optimal models. It ensures high confidence in model quality while minimizing computational costs for complex optimization tasks.

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    Area of Science:

    • Machine Learning
    • Optimization
    • Statistical Inference

    Background:

    • Multi-objective model selection (MOMS) involves optimizing multiple, often conflicting, objectives simultaneously.
    • Identifying Pareto-optimal models is crucial for robust decision-making in complex systems.
    • Existing methods may struggle with computational efficiency and statistical guarantees in stochastic environments.

    Purpose of the Study:

    • Introduce SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting.
    • Address MOMS with multiple stochastic optimization objectives in the Pareto-optimality sense.
    • Minimize computational effort while ensuring a prescribed confidence level in model quality.

    Main Methods:

    • Utilizes a sequential probability ratio with an indifference zone test for pairwise dominance assessment.
    • Employs a non-parametric, ternary-decision, dual-sequential probability ratio test for statistical inference.
    • Implements Holm's step-down family-wise error rate control for strict error bounds.

    Main Results:

    • SPRINT-Race statistically infers dominance relationships with controlled error rates.
    • Demonstrates effectiveness on an artificial MOMS problem with known ground truth.
    • Shows efficiency and effectiveness in real-world applications like recommender systems and stock selection.

    Conclusions:

    • SPRINT-Race is an effective and efficient tool for multi-objective model selection.
    • Provides a fixed-confidence approach to MOMS, balancing performance and computational cost.
    • The algorithm offers a statistically rigorous method for identifying optimal models in complex scenarios.