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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Differential Leveling01:12

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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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Related Experiment Video

Updated: Oct 17, 2025

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.4K

Evolving Gradient Boost: A Pruning Scheme Based on Loss Improvement Ratio for Learning Under Concept Drift.

Kun Wang, Jie Lu, Anjin Liu

    IEEE Transactions on Cybernetics
    |October 6, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Gradient boosting models struggle with concept drift. A new loss improvement ratio (LIR) metric and pruning strategies enable evolving gradient boost (LIR-eGB) to effectively adapt to changing data distributions.

    Related Experiment Videos

    Last Updated: Oct 17, 2025

    Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
    07:34

    Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

    Published on: August 22, 2018

    8.4K

    Area of Science:

    • Machine Learning
    • Data Science
    • Artificial Intelligence

    Background:

    • Nonstationary environments exhibit changing data distributions, a phenomenon known as concept drift.
    • Maintaining accuracy in gradient boosting (GB) ensemble models under concept drift requires effective methods for selecting and pruning weak learners.
    • Existing methods like AdaBoost face challenges in GB due to varying performance scales of weak learners.

    Purpose of the Study:

    • To propose a novel criterion, the loss improvement ratio (LIR), for evaluating weak learners in GB models, addressing performance measurement scaling issues.
    • To develop and implement adaptive pruning strategies for GB models to enhance performance in nonstationary environments.
    • To introduce an evolving gradient boost (LIR-eGB) algorithm that dynamically adjusts pruning based on LIR to combat concept drift.

    Main Methods:

    • Introduced the loss improvement ratio (LIR) to standardize weak learner performance evaluation in GB.
    • Developed two pruning strategies: naive pruning (NP) and statistical pruning (SP), based on LIR.
    • Designed a dynamic switching scheme between NP and SP, integrated into the LIR-eGB algorithm.

    Main Results:

    • LIR-eGB demonstrated superior performance compared to state-of-the-art methods on both stationary and nonstationary datasets.
    • The proposed LIR criterion effectively addresses the challenge of comparing weak learners on different scales within GB models.
    • Dynamic switching between NP and SP strategies optimized model adaptation to concept drift.

    Conclusions:

    • The LIR criterion and associated pruning strategies provide an effective solution for adapting GB models to concept drift.
    • LIR-eGB offers a robust and high-performing algorithm for machine learning in nonstationary environments.
    • This research advances the field of adaptive learning by providing a novel approach to handling concept drift in ensemble models.