MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler (2010.08830v1)

Abstract: Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed based on some heuristic assumptions. They often suffer from unstable performance, poor applicability, and high computational cost in complex tasks where their assumptions do not hold. In this paper, we introduce a novel ensemble IL framework named MESA. It adaptively resamples the training set in iterations to get multiple classifiers and forms a cascade ensemble model. MESA directly learns the sampling strategy from data to optimize the final metric beyond following random heuristics. Moreover, unlike prevailing meta-learning-based IL solutions, we decouple the model-training and meta-training in MESA by independently train the meta-sampler over task-agnostic meta-data. This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks. Extensive experiments on both synthetic and real-world tasks demonstrate the effectiveness, robustness, and transferability of MESA. Our code is available at https://github.com/ZhiningLiu1998/mesa.

• The paper introduces a meta-sampler that learns an optimal sampling strategy using reinforcement learning to improve performance on imbalanced datasets.
• It decouples meta-training from model training, increasing flexibility across classifiers and reducing computational costs.
• Experimental results show that MESA outperforms traditional methods, particularly in handling overlapping classes and achieving higher AUCPRC.

An Overview of "MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler"

The paper "MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler" introduces MESA, an innovative approach to address challenges in imbalanced learning (IL). Imbalanced learning is a prominent problem in many real-world applications like click prediction, fraud detection, and medical diagnosis, where datasets often have skewed class distributions, resulting in biased models that underperform particularly on minority classes. Traditional methods such as resampling and reweighting have limitations in performance stability, applicability, and computational efficiency, primarily due to heuristic assumptions that often do not hold in complex scenarios.

Proposed MESA Framework

MESA is an ensemble-based IL solution that advances previous methodologies by incorporating a meta-learning mechanism. The framework consists of three primary components: meta-sampling, ensemble training, and meta-training. The meta-sampling component learns an optimal sampling strategy directly from data by modeling the sampling process as an optimization task rather than relying on heuristically predefined methods.

The meta-sampler within MESA differentiates itself by employing reinforcement learning (RL) principles to improve performance. It decouples model training from meta-training, thereby allowing broad applicability to various classifiers, including decision trees, Naïve Bayes, and k-nearest neighbors. This independence also allows the meta-sampler to generalize and transfer its learning across tasks, reducing computational costs associated with meta-training on new data.

Experimental Validation

The efficacy of MESA has been validated against both synthetic and real-world datasets. Results from experiments demonstrate MESA's capability to outperform conventional methods in terms of the area under the precision-recall curve (AUCPRC). The MESA framework shows strong performance, particularly in handling overlapping class distributions, noise robustness, and understanding minority class representation.

The paper includes comprehensive experiments comparing MESA with other ensemble imbalanced learning methods. It highlights MESA's ability to transfer meta-trained samplers to new tasks effectively, which is a significant advantage in practical applications where obtaining a large, correctly labeled dataset for each new task presents a bottleneck.

Technical Contributions and Implications

1. Meta-Sampling Strategy: MESA's core innovation lies in adapting the sampling strategy through RL, which optimizes the generalization of ensemble models directly from the data rather than relying on predefined heuristics.
2. Decoupled Training Process: By decoupling the meta-sampler's training from the task-specific classification models, it affords greater flexibility and applicability across different learning tasks and models.
3. Cross-Task Transferability: MESA's design allows the meta-sampler to be pre-trained on one task and applied to others, significantly reducing the computational resources required during deployment on new tasks.

In conclusion, MESA makes significant strides in the domain of imbalanced learning by leveraging a meta-learning-based approach to optimize the training data selection process in ensemble classifiers. This framework is not only effective at improving classification performance but also efficient in its use of computational resources, paving the way for broader adoption in various application domains. Future work is likely to explore extending these concepts to tackle multiclass imbalanced datasets and to integrate more advanced reinforcement learning techniques to further improve the meta-sampler's efficacy.