Very Simple Classifier: a Concept Binary Classifier toInvestigate Features Based on Subsampling and Localility

Ensemble Wrapper Subsampling for Deep Modulation Classification

Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output samples. We propose a subsampling technique to facilitate the use of deep learning for automatic modulation classification in wireless communication systems. Unlike traditional approaches that rely on pre-designed strategies that are solely based on expert knowledge, the proposed data-driven subsampling strategy employs deep neural network architectures to simulate the effect of removing candidate combinations of samp…

Subsampling Bias and The Best-Discrepancy Systematic Cross Validation

Statistical machine learning models should be evaluated and validated before putting to work. Conventional k-fold Monte Carlo Cross-Validation (MCCV) procedure uses a pseudo-random sequence to partition instances into k subsets, which usually causes subsampling bias, inflates generalization errors and jeopardizes the reliability and effectiveness of cross-validation. Based on ordered systematic sampling theory in statistics and low-discrepancy sequence theory in number theory, we propose a new k-fold cross-validation procedure by replacing a pseudo-random sequence with a best-discrepancy seque…

Contingency Training

When applied to high-dimensional datasets, feature selection algorithms might still leave dozens of irrelevant variables in the dataset. Therefore, even after feature selection has been applied, classifiers must be prepared to the presence of irrelevant variables. This paper investigates a new training method called Contingency Training which increases the accuracy as well as the robustness against irrelevant attributes. Contingency training is classifier independent. By subsampling and removing information from each sample, it creates a set of constraints. These constraints aid the method to …

CLIMAX：基于分类器的对比解释探索

CLIMAX: An exploration of Classifier-Based Contrastive Explanations

本文提出 CLIMAX 事后模型无关解释方法，通过标签感知替代数据生成和影响子采样构建局部分类器，为黑盒分类器提供对比解释，并验证其一致性和在文本、图像数据上的有效性。

Slot-Mixup 与子采样：一种用于 WSI 分类的简单正则化方法

Slot-Mixup with Subsampling: A Simple Regularization for WSI Classification

本文针对 WSI 分类中弱监督导致的过拟合问题，提出子采样增强与 Slot-MIL 注意力槽位模型，并结合 mixup，在多个基准上取得最优性能并提升泛化与校准能力。

特征加权最大代表性子抽样

Feature-Weighted Maximum Representative Subsampling

针对部分特征高度偏倚导致代表性变量引入偏差的问题，提出特征加权最大代表性子抽样（FW-MRS）方法，通过特征权重降低高度偏倚特征的影响，在保持下游任务泛化性能的同时保留更多实例。