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    Area of Science:

    • Machine Learning
    • Data Science
    • Artificial Intelligence

    Background:

    • Dataset labeling is crucial for machine learning but often expensive and time-consuming.
    • Conventional methods require numerous pairwise comparisons per sample, leading to inefficiency.
    • Optimizing expert oracle queries is essential for practical machine learning applications.

    Purpose of the Study:

    • To develop and analyze efficient k-ary query schemes for dataset labeling.
    • To reduce the number of queries required from an expert oracle.
    • To improve the overall efficiency of the machine learning data labeling process.

    Main Methods:

    • Introduced k-ary query schemes (k ≥ 2) to identify (dis)similar items and leverage transitive relations.
    • Developed a randomized batch algorithm operating in rounds for sample labeling.
    • Proposed an adaptive greedy query scheme utilizing triplet queries.

    Main Results:

    • The randomized batch algorithm achieves a query rate of [Formula: see text].
    • The adaptive greedy scheme achieves an average rate of approximately 0.2N queries per sample with triplet queries.
    • Empirical studies show triplet queries are only ~50% slower than pairwise queries, demonstrating effectiveness.

    Conclusions:

    • K-ary query schemes offer a significant improvement in efficiency for expert-based dataset labeling.
    • The proposed algorithms effectively reduce the query load on expert oracles.
    • These methods are adaptable to nonuniform class distributions, enhancing their practical utility.