Support vector machines have been progressively refined in existing research, but there are still certain limitations in feature selection for high-dimensional data.
This paper proposes an adaptive-threshold max-SVM model, which seeks to improve the accuracy and effectiveness of classification and identifies key features. The model comprises a set of SVM sub-models, each trained on a specific feature combination and adjusting its decision boundary with adaptively optimized thresholds. All sub-models independently generate classification results, and the final decision is determined by selecting the maximum value from these outputs.
In biomedical data analysis, this model achieves high accuracy with 100\% sensitivity. It delivers superior performance over conventional models in repeated cross-validation and exhibits strong generalization and robustness, which are also validated on non-biomedical datasets.
This paper designs a novel adaptive-threshold max-SVM model equipped with the Progressive-Iterative Algorithm. The algorithm consists of a three-step screening and iterative construction process that identifies an optimal combination of sub-models from a large candidate pool. It enables efficient feature selection, delivering favorable classification performance, generalization ability and robustness.
