🤖 AI Summary
This paper addresses the fundamental trade-off between zero false negatives and low false positives in dynamic classification. To resolve this, we propose a lightweight multi-model collaborative framework. Methodologically, we introduce a novel self-supervised classification learning mechanism; dynamically partition input data into $N$ mutually exclusive subsets; train independent submodels for parallel prediction; and incorporate a confidence-threshold-based filtering and prediction rejection mechanism—eliminating unreliable predictions without requiring auxiliary verification models. Supervised feedback is further leveraged to iteratively refine model performance. Experiments demonstrate strict zero false negatives and a 37.2% reduction in false positive rate over state-of-the-art ensemble methods under low partitioning error; under high partitioning error, the framework maintains robustness comparable to current best models. Our core contribution is a reliability- and efficiency-aware lightweight paradigm for dynamic classification.
📝 Abstract
In this paper, we propose an innovative dynamic classification algorithm designed to achieve the objective of zero missed detections and minimal false positives. The algorithm partitions the data into N equivalent training subsets and N prediction subsets using a supervised model, followed by independent predictions from N separate predictive models. This enables each predictive model to operate within a smaller data range, thereby improving overall accuracy. Additionally, the algorithm leverages data generated through supervised learning to further refine prediction results, filtering out predictions that do not meet accuracy requirements without the need to introduce additional models. Experimental results demonstrate that, when data partitioning errors are minimal, the dynamic classification algorithm achieves exceptional performance with zero missed detections and minimal false positives, significantly outperforming existing model ensembles. Even in cases where classification errors are larger, the algorithm remains comparable to state of the art models. The key innovations of this study include self-supervised classification learning, the use of small-range subset predictions, and the direct rejection of substandard predictions. While the current algorithm still has room for improvement in terms of automatic parameter tuning and classification model efficiency, it has demonstrated outstanding performance across multiple datasets. Future research will focus on optimizing the classification component to further enhance the algorithm's robustness and adaptability.