Credal Dog-4

Most datasets used for classification use hard labels. Credal Dog-4 was labeled uncertainly and imprecisely by contributors during crowdsourcing campaigns. Resulting in richer labels, modeled with the theory of belief funtions, which generalizes several reasoning frameworks with uncertainty. These datasets can be used with classical models using hard labels but also with probabilistic, fuzzy or even evidential models. Dataset: 4 classes, 400 observations, 47 features (or raw pictures) When using the dataset please cite : A. Hoarau, C. Thierry, A. Martin, J.-C. Dubois, Y. Le Gall, "Datasets with rich labels for machine learning", in: FUZZ-IEEE, 2023. Credal Dog-4 dataset ├── data │ ├── classes.csv: Classes of the dataset │ ├── X.csv: Features of the dataset │ ├── X_512.csv: Learge 512 features vector of the dataset │ ├── X_pictures.csv: Raw features (Pictures themselves) │ ├── y.csv: Rich labels │ └── y_true.csv: True labels └── extra ├── y_hard.csv: Hard labels given during a new campaign ├── DATA_imperfect.csv: Imperfect answers given during the campaign ├── ITERATION_imperfect.csv: Imperfect 2nd step answers given during the campaign ├── EVENT_imperfect.csv: Contributors events ├── ID_imperfect.csv: Contributors IDs └── DATA_perfect.csv: Precise answers given during a new campaign Credal Dog-7: https://data.mendeley.com/datasets/4hz3wx6wm5 Credal Dog-2: https://data.mendeley.com/datasets/95mmjfrsyh Credal Bird-10: https://data.mendeley.com/datasets/9sz5vm8g7h Credal Bird-2: https://data.mendeley.com/datasets/v8k6c5s9cy当前用于分类任务的绝大多数数据集均采用硬标签（hard labels）。Credal Dog-4数据集在众包标注活动中由标注者生成了不确定且不精确的标签，由此得到的富标签（rich labels）基于信任函数理论（theory of belief functions）建模，该理论可泛化多种不确定性推理框架。该数据集不仅可与采用硬标签的经典机器学习模型适配，同样可用于概率模型、模糊模型乃至证据推理模型。 数据集参数：共4个类别、400条观测样本、47维特征（亦可采用原始图像作为特征）。 使用本数据集时，请引用以下文献：A. Hoarau、C. Thierry、A. Martin、J.-C. Dubois、Y. Le Gall，"Datasets with rich labels for machine learning"，收录于：FUZZ-IEEE 2023。 Credal Dog-4数据集文件结构如下： ├── data │ ├── classes.csv：数据集类别信息 │ ├── X.csv：数据集特征数据 │ ├── X_512.csv：数据集的512维高维特征向量 │ ├── X_pictures.csv：原始图像特征（即原始图片本身） │ ├── y.csv：富标签数据 │ └── y_true.csv：真实标签数据 └── extra ├── y_hard.csv：新一轮众包流程中生成的硬标签 ├── DATA_imperfect.csv：首轮众包流程中得到的不精确标注结果 ├── ITERATION_imperfect.csv：第二轮众包流程中得到的不精确标注结果 ├── EVENT_imperfect.csv：标注者事件记录 ├── ID_imperfect.csv：标注者身份标识 └── DATA_perfect.csv：新一轮众包流程中得到的精确标注结果 其余相关数据集链接如下： Credal Dog-7：https://data.mendeley.com/datasets/4hz3wx6wm5 Credal Dog-2：https://data.mendeley.com/datasets/95mmjfrsyh Credal Bird-10：https://data.mendeley.com/datasets/9sz5vm8g7h Credal Bird-2：https://data.mendeley.com/datasets/v8k6c5s9cy