What issues could an imbalance of labels in the training set rise?
Best insight from top research papers
An imbalance of labels in the training set can lead to several issues. Firstly, it can significantly degrade the performance of existing methods in label distribution learning (LDL) tasks . Secondly, it can result in noisy labels due to high disagreement among annotators, affecting the performance of machine learning models . Thirdly, class-imbalance, which is a common property of multi-label data, can affect the generalization performance of multi-label predictive models . Lastly, the imbalance of labels can further aggravate the class imbalance in multi-label learning, making it harder to obtain accurate predictions .
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| Papers (5) | Insight |
|---|---|
4 Citations | The imbalance of labels in the training set can aggravate the class imbalance in multi-label learning, leading to lower identification accuracy of minority labels. |
12 Citations | The imbalance of labels in the training set can significantly affect the generalization performance of the multi-label predictive model. |
| The paper does not explicitly mention the issues that an imbalance of labels in the training set can cause. | |
| The imbalance of labels in the training set can lead to a decrease in classification accuracy, especially for the minority class. This issue is addressed in the paper by using a weight loss-based class distribution resampling to improve model performance. | |
| The paper mentions that an imbalance of labels in the training set can significantly degrade the performance of existing methods for Label Distribution Learning (LDL). |
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