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Source note2 min read

Instance Hardness-Based Relevance for Imbalanced Regression

Source

arXiv

arxiv.org/abs/2607.20173v1

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Source

Plain-Language Brief

The paper proposes a new relevance function called Instance Hardness-based relevance (InHaR) for identifying rare instances in imbalanced regression problems. This function takes into account the learning difficulty of instances, which is particularly useful in complex scenarios like bimodal distributions where traditional relevance functions struggle to capture rarity. Experimental results show that using InHaR leads to significant improvements in predictive performance compared to traditional relevance-based approaches.

What the Document Says

  • The text discusses imbalanced regression problems and the limitations of traditional relevance functions in complex scenarios like bimodal distributions.
  • A new Instance Hardness-based relevance function (InHaR) is proposed to identify rare instances by incorporating learning difficulty.
  • Experimental results show that InHaR correctly identifies rare regions under bimodal distributions and improves predictive performance.

Key Concepts Mentioned

  • Imbalanced regression
  • Instance Hardness-based relevance (InHaR)
  • Bimodal distribution
  • Learning difficulty
  • Predictive performance

Claims Made by the Document

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

Evidence Used

  • The text provides experimental results demonstrating the effectiveness of InHaR.

Methods / Approach

  • Instance Hardness-based relevance function (InHaR)

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

It's unclear how the proposed method performs on datasets other than bimodal distributions.

Open Questions

  • How does InHaR perform on datasets other than bimodal distributions?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text doesn't provide information about the specific datasets used in the experiments or how they were generated. It also doesn't discuss any potential limitations of the proposed method.

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.