This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a context of Handwritten Signature Verification (HSV). SigNet is a state of the art Deep CNN model for feature representation in the HSV context and contains 2048 dimensions. Some of these dimensions may include redundant information in the dissimilarity representation space generated by the dichotomy transformation (DT) used by the writer-independent (WI) approach. The analysis is carried out on the GPDS-960 dataset. Experiments demonstrate that the proposed method is able to control overfitting during the search for the most discriminant representation.

中文翻译：

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通过过拟合控制改进基于 BPSO 的特征选择应用于离线 WI 手写签名验证

本文研究了使用二元粒子群优化 (BPSO) 在手写签名验证 (HSV) 上下文中执行特征选择时是否存在过度拟合。SigNet 是最先进的深度 CNN 模型，用于 HSV 上下文中的特征表示，包含 2048 个维度。其中一些维度可能包括由作者独立 (WI) 方法使用的二分变换 (DT) 生成的相异性表示空间中的冗余信息。分析是在 GPDS-960 数据集上进行的。实验表明，所提出的方法能够在搜索最具判别力的表示过程中控制过拟合。