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Related Concept Videos

Mean Absolute Deviation01:13

Mean Absolute Deviation

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The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
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Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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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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Weighted Mean00:57

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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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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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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An Adaptive Deep Metric Learning Loss Function for Class-Imbalance Learning via Intraclass Diversity and Interclass

Jie Du, Xiaoci Zhang, Peng Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |June 28, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the intraclass diversity and interclass distillation (IDID) loss to address data scarcity and density in deep metric learning. IDID-loss improves feature representation and generalization, outperforming existing methods on real-world datasets.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Deep metric learning (DML) effectively extracts discriminant features but struggles with class-imbalance learning (CIL) issues like data scarcity and density.
    • Existing DML and CIL losses fail to simultaneously address feature overlapping, data scarcity, and data density, leading to misclassification.

    Purpose of the Study:

    • To propose a novel loss function, intraclass diversity and interclass distillation (IDID) loss with adaptive weight, capable of mitigating DML and CIL challenges concurrently.
    • To enhance feature representation by generating diverse intra-class features and preserving inter-class semantic correlations.

    Main Methods:

    • Developed the IDID loss function, which promotes intra-class diversity irrespective of sample size to combat data scarcity and density.
    • Implemented learnable similarity within IDID loss to maintain semantic correlations between classes while pushing dissimilar classes apart, reducing overlapping.
    • Introduced an adaptive weight mechanism to balance the contributions of diversity and distillation components.

    Main Results:

    • IDID loss successfully mitigates data scarcity, data density, and feature overlapping simultaneously, outperforming traditional DML and CIL losses.
    • The proposed method generates more diverse and discriminant feature representations, leading to improved generalization ability.
    • Experiments on seven public datasets demonstrated superior performance in G-mean, F1-score, and accuracy, with significant improvements on imbalanced classes.

    Conclusions:

    • The IDID loss offers a unified solution for deep metric learning in the presence of class imbalance, data scarcity, and density.
    • This approach eliminates the need for time-consuming hyperparameter fine-tuning, simplifying practical application.
    • IDID loss provides a robust and effective method for enhancing classification performance in challenging real-world scenarios.