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Source note3 min read

Instance Hardness-Based Relevance for Imbalanced Regression

Source

arXiv

arxiv.org/abs/2607.20173v1

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Source

Plain-Language Brief

A new approach called Instance Hardness-based relevance function (InHaR) is proposed to identify rare instances in regression problems, particularly useful for complex scenarios like bimodal distributions where traditional methods struggle. The InHaR takes into account learning difficulty along with the target distribution, and experimental results show significant improvements in predictive performance when used with resampling strategies.

What the Document Says

  • The text discusses a study that introduces an Instance Hardness-based relevance function (InHaR) for identifying rare instances in regression problems.
  • InHaR is particularly useful for scenarios like bimodal distributions where traditional methods fail to accurately capture rarity.
  • Unlike traditional methods, InHaR considers learning difficulty along with the target distribution.
  • The study demonstrates that InHaR correctly identifies rare regions under bimodal distributions and improves predictive performance when used with resampling strategies like Random Oversampling (RO) and Gaussian Noise (GN).
  • The code, dataset, and further details about the proposed method are available on GitHub.

Key Concepts Mentioned

  • Instance Hardness-based relevance function (InHaR)
  • Imbalanced regression problems
  • Bimodal distributions
  • Learning difficulty

Claims Made by the Document

  • Traditional relevance functions struggle to capture rarity in complex scenarios.
  • The InHaR correctly identifies rare regions under bimodal distributions.
  • Using InHaR leads to significant improvements in predictive performance.

Evidence Used

  • Experimental results demonstrate that the InHaR correctly identifies rare regions under bimodal distributions and improves predictive performance.

Methods / Approach

  • Instance Hardness-based relevance function (InHaR)

Limitations Stated by the Authors

  • The effectiveness of imbalance-aware learning methods depends strongly on how relevance is defined.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The text does not provide information about the specific complexities or scenarios where traditional methods may fail other than bimodal distributions.

Open Questions

  • What are the specific complexities or scenarios where traditional methods may fail other than bimodal distributions?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The text mentions 'Random Oversampling (RO)' and 'Gaussian Noise (GN)', but does not specify whether these are the only resampling strategies used or if they were compared with other strategies.

Raw Abstract / Excerpt

Imbalanced regression problems arise when the target variable has an asymmetric distribution, resulting in underrepresented value ranges in the dataset. Traditional approaches for identifying rare instances rely on a relevance function that assigns higher importance to specific regions of the target distribution. However, the effectiveness of imbalance-aware learning methods depends strongly on how relevance is defined. In more complex scenarios, such as bimodal distributions, traditional relevance functions struggle to capture rarity, as they assign fixed relevance values based solely on target values, thereby compromising the distinction between truly rare and normal instances. To address these limitations, this study proposes an Instance Hardness-based relevance function (InHaR) for identifying rare instances in regression problems. Unlike traditional relevance functions, the proposed approach incorporates learning difficulty, allowing rarity to be inferred not only from the target distribution but also from the difficulty of instances for the learning algorithm. This property is particularly important in bimodal scenarios, where rarity cannot be accurately inferred from target values alone. Experimental results demonstrate that the InHaR correctly identifies rare regions under bimodal distributions and, when used to guide resampling strategies such as Random Oversampling (RO) and Gaussian Noise (GN), leads to significant improvements in predictive performance compared to traditional relevance-based approaches. The code, dataset, and further details about the proposed method are publicly available at https://github.com/VitorLeitao/instance-hardness-Imbalanced-regression.