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Enriched multi-view ensemble approach for high-dimensional imbalanced data classification
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AI
DOI：10.1016/j.engappai.2026.113940.png)
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Abstract
En 中文
High-dimensional imbalanced data classification is a challenging issue in real-world applications, where massive invalid features and class imbalance severely impede the behavior of classifiers. Due to high-dimensional features, imbalanced approaches suffer hardship in yielding adequate results. To tackle these issues, this paper proposes an enriched multi-view ensemble approach (EMEA), aiming to construct an accurate and resilient classifier ensemble system for high-dimensional class-skewed data. First, an enriched multi-view optimization (EMO) is designed to extract effective and diverse features from high-dimensional imbalanced data, it promotes the classification ability through subview learning on multiple diverse scenarios. Then a prioritized integration of subviews (PIS) is developed to conduct selective integration for subviews, aiming to construct a high-quality view that enhances decision-making for high-dimensional imbalanced data classification. Finally, EMEA employs resampling to construct a balanced subset, mitigating the impact of class imbalance on the base classifier. The experiments on 16 high-dimensional class-skewed datasets demonstrate that EMEA is superior to other mainstream imbalanced ensemble approaches.
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