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Nested evolution for interactively fusing feature agents and learning ensembled classifier agents
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AI
DOI：10.1016/j.patcog.2025.112837.png)
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Abstract
En 中文
Currently, feature selection methods combined with classifiers face challenges due to a lack of interactivity and interpretability. These issues lead to suboptimal classification performance. Inspired by the continuous learning process among agents through mutual feedback, we propose an innovative nested evolutionary framework. In this framework, we innovatively propose the concepts of feature agent and classifier agent, representing the feature selection and classifier construction and evaluation processes, respectively. Furthermore, we introduce macro-evolution and micro-evolution mechanisms to facilitate interactive learning between the processes. Specifically, during the macro-evolution phase, a multi-objective evolutionary biclustering algorithm is employed to generate multiple biclusters (feature subsets), thereby completing the learning process of the feature agents. Subsequently, classification rules are extracted from these biclusters to construct weak classifiers, which are further evaluated and optimized to achieve the learning process of the classifier agents. In the micro-evolution phase, the evaluation results of the weak classifiers are used as feedback to re-evolve the biclusters corresponding to the underperforming weak classifiers, resulting in improved feature subsets and thereby enhancing the performance of the weak classifiers. This iterative process achieves interactive learning between the feature agents and classifier agents, thereby simultaneously improving both the feature subsets and classifiers. Finally, we employ AdaBoost to integrate the weak classifiers into a robust strong classifier. Experimental results demonstrate that it outperforms other methods across multiple binary classification datasets.
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