ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization

Priel Hazan; Nir Almog; Noam Keidar; Yael Yaniv; Assaf Schuster

cs.LGOct 11, 2026

ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization

Authors: Priel Hazan, Nir Almog, Noam Keidar, Yael Yaniv, Assaf Schuster

Organizations: Technion – Israel Institute of Technology, Haifa, Israel

Abstract

Real-world datasets often exhibit severe class imbalance, degrading the performance of deep learning models, particularly on rare classes. While imbalance is well studied in binary and multiclass settings, it is substantially more challenging in multilabel problems due to complex label co-occurrence. In clinical applications such as ECG classification, this challenge is coupled with a second critical challenge: the need to prioritize high-risk conditions, regardless of their prevalence, to mitigate the high cost of false negatives. We introduce the Multilabel Class-Prioritized Probabilistic Sampler (ML-CLAPPS), a learnable sampler that optimizes a distribution over training instances to control the model's exposure to different classes. This approach addresses multilabel imbalance while enabling prioritization of significant conditions via a user-defined importance vector (IV). Empirical evaluation on a real-world multilabel ECG image dataset demonstrates that ML-CLAPPS achieves state-of-the-art performance. We show that it aligns class exposure with the specified IV, and that increasing a class's importance leads to corresponding improvements in its classification performance, enabling explicit prioritization of clinically critical conditions.

Figures & tables

Figure 1: Class distribution of the Mobile dataset, illustrating dataset imbalance.

Figure 2: Optimal $\mathbf{p}^{\star}$ (left) and $\mathbf{d}$ (right) from ML-CLAPPS under varying $\gamma$ with uniform IV. Larger $\gamma$ improves dataset utilization via more uniform sampling, at the cost of shifting exposure toward the original imbalanced label distribution (Fig. 1 ).

Figure 3: Effect of $\gamma$ on dataset utilization loss (left axis) and alignment loss (right axis), derived from solving ML-CLAPPS for each $\gamma$ under a uniform IV. Lower values denote improved utilization and closer exposure-importance alignment.

Figure 4: Expected class exposure $\mathbf{d}$ obtained by solving ML-CLAPPS for different IVs using $\gamma=10^{3}$ . Top: descending importance. Bottom: alternating importance. Expected exposure closely follows the prescribed class priorities, mirroring the user-defined IVs.

Method	Classification Performance			Overhead (sec) $\downarrow$
	F1 $\uparrow$	MCC $\uparrow$	Accuracy $\uparrow$	Init	Per-Epoch
UniformSampler (Baseline)	0.281 $\pm$ .02	0.301 $\pm$ .02	0.954 $\pm$ .001	0	0
LP ROS [ 6 , 13 ]	0.291 $\pm$ .01	0.315 $\pm$ .01	0.949 $\pm$ .001	0.05 $\pm$ .01	0
ClusterClip Sampling [ 24 ]	0.299 $\pm$ .02	0.344 $\pm$ .03	0.953 $\pm$ .002	0.01 $\pm$ .00	0.01 $\pm$ .00
LeastSampledClass [ 19 ]	0.325 $\pm$ .02	0.365 $\pm$ .02	0.954 $\pm$ .001	0	0.49 $\pm$ .05
ML-CLAPPS (IV= LinR2F )	0.347 $\pm$ .01	0.363 $\pm$ .01	0.954 $\pm$ .001	1.17 $\pm$ .20	0

Table 1: Comparative performance of sampling strategies on the Mobile ECG dataset. ML-CLAPPS outperforms prior methods. Table reports mean

\pm

SD across four runs.

Figure 5: The x-axis shows class prevalence from rare (left) to frequent (right), where $R_{k}$ indicates the kth most rare class. The y-axis reports mean F1 across four runs. For each sampling method, a fitted linear regression line is shown to highlight performance trends across class frequencies. LinR2F IV demonstrates targeted condition prioritization.

Multi-Label Classification

Multi-label classification assigns several potentially related tags to each example, from medical findings and topics to objects and user interests.

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Addressing Imbalance in Multi-Label Data via Label-Specific Distance-based Oversampling

Bin Liu, Jun Wu, Haoyu Peng, Ao Zhou+3

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The complex imbalanced label distribution poses a crucial challenge to multi-label classification, as most classifiers are biased towards the majority class and high-frequent labels. Oversampling is an efficient and flexible solution that augments instances to provide a more balanced training dataset for multi-label classifiers. Most existing oversampling methods create synthetic instances in a heuristic way that essentially relies on neighborhood information retrieved using Euclidean distance within the entire feature space. However, they fail to consider the varying semantic relevance of features to different labels, leading to label inconsistency among proximate neighbors and further introducing label confusion and overfitting to synthetic instances. To overcome the above issue, we propose a novel sampling approach called Label-Specific Distance-based Multi-Label Oversampling (LSDMLO) that creates more useful and well-labeled synthetic instances to address the imbalance in multi-label datasets. LSDMLO derives the label-specific distance to identify label-consistent neighbors based on the weighted pertinent feature space, which facilitates selecting seed instances that express more label correlations in boundary areas and generating synthetic instances aligned with the label distribution of original data. The comprehensive experiments verify that the proposed LSDMLO outperforms the state-of-the-art multi-label sampling approaches under various base classifiers.

Abstract

Multi-Label Classification

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