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On Bayesian Network Classifiers with Reduced Precision Parameters.

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    We analyzed reduced precision Bayesian network classifiers (BNCs) for embedded systems. Discriminatively optimized parameters offer better classification rates, while generatively optimized parameters show more robustness at very low bit-widths.

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    Area of Science:

    • Machine Learning
    • Computer Engineering

    Background:

    • Bayesian network classifiers (BNCs) are typically used on desktop computers.
    • Real-world applications increasingly require BNCs on embedded or low-power systems, an area lacking rigorous study.

    Purpose of the Study:

    • To analyze the impact of reduced precision implementations on BNCs.
    • To investigate parameter quantization effects on classification rate (CR) for discrete-valued BNCs.

    Main Methods:

    • Derived worst-case and probabilistic bounds on CR for various bit-widths.
    • Evaluated bounds on benchmark datasets.
    • Compared performance and robustness of generatively vs. discriminatively optimized parameters under quantization.
    • Analyzed margin-optimized Tree Augmented Network (TAN) structures.

    Main Results:

    • Generatively optimized parameters are more robust at very low bit-widths, with fewer classification changes due to quantization.
    • Discriminatively optimized parameters yield better classification performance across most bit-widths.
    • Margin-optimized TAN structures outperform generatively optimized TANs in CR and robustness.

    Conclusions:

    • Reduced precision BNCs are feasible for embedded systems.
    • Parameter optimization strategy significantly impacts performance and robustness trade-offs.
    • Margin-optimized TANs present a promising direction for efficient BNC deployment.