imbalanced classification

The Kinetics dataset is a large-scale, high-quality dataset for human action recognition in videos. The dataset consists of around 500,000 video clips covering 600 human action classes with at least 600 video clips for each action class. Each video clip lasts around 10 seconds and is labeled with a single action class. The videos are collected from YouTube. Source: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey

CI-MNIST (Correlated and Imbalanced MNIST) is a variant of MNIST dataset with introduced different types of correlations between attributes, dataset features, and an artificial eligibility criterion. For an input image $x$, the label $y \in \\{1, 0\\}$ indicates eligibility or ineligibility, respectively, given that $x$ is even or odd. The dataset defines the background colors as the protected or sensitive attribute $s \in \\{0, 1\\}$, where blue denotes the unprivileged group and red denotes the privileged group. The dataset was designed in order to evaluate bias-mitigation approaches in challenging setups and be capable of controlling different dataset configurations.

Problem Statement Material backorder is a common problem in a supply chain system, impacting an inventory system's service level and effectiveness. Identifying parts with the highest chances of shortage prior to their occurrence can present a high opportunity to improve an overall company’s performance. In this project, we will train classifiers to predict future back-ordered products and generate predictions for a test set. File descriptions Here we have two CSV files (Training_BOP.csv and Testing_BOP.csv) Training_BOP.csv - the training set Testing_BOP.csv - the testing set Each file has 23 columns; the last column (went_on_backorder) is the target column. Data fields sku - sku code national_inv - Current inventory level of component lead_time - Transit time in_transit_qty - Quantity in transit forecast_x_month - Forecast sales for the net 3, 6, and 9 months sales_x_month - Sales quantity for the prior 1, 3, 6, and 9 months min_bank - Minimum recommended amount in stock potential_issue - Indicator variable noting a potential issue with the item pieces_past_due - Parts overdue from the source perf_x_months_avg - Source performance in the last 6 and 12 months local_bo_qty - Amount of stock orders overdue x17-x22 - General Risk Flags went_on_back_order - Product went on backorder Validation - indicator variable for training (0), validation (1), and test set (2)

Imbalanced-MiniKinetics200 was proposed by "Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition" to evaluate varying scenarios of video long-tailed recognition. Similar to CIFAR-10/100-LT, it utilizes an imbalance factor to construct long-tailed variants of the MiniKinetics200 dataset. Imbalanced-MiniKinetics200 is a subset of Mini-Kinetics-200 consisting of 200 categories which is also a subset of Kinetics400. Both the raw frames and extracted features with ResNet50/101 are provided.

This is a real-world industrial benchmark dataset from a major medical device manufacturer for the prediction of customer escalations. The dataset contains features derived from IoT (machine log) and enterprise data including labels for escalation from a fleet of thousands of customers of high-end medical devices. The dataset accompanies the publication "System Design for a Data-driven and Explainable Customer Sentiment Monitor" (submitted). We provide an anonymized version of data collected over a period of two years. The dataset should fuel the research and development of new machine learning algorithms to better cope with real-world data challenges including sparse and noisy labels, and concept drifts. Additional challenges are the optimal fusion of enterprise and log-based features for the prediction task. Thereby, the interpretability of designed prediction models should be ensured in order to have practical relevancy. Supporting software Kindly use the corresponding GitHub repository (https://github.com/annguy/customer-sentiment-monitor) to design and benchmark your algorithms. Citation and Contact If you use this dataset please cite the following publication: @ARTICLE{9520354, author={Nguyen, An and Foerstel, Stefan and Kittler, Thomas and Kurzyukov, Andrey and Schwinn, Leo and Zanca, Dario and Hipp, Tobias and Jun, Sun Da and Schrapp, Michael and Rothgang, Eva and Eskofier, Bjoern}, journal={IEEE Access}, title={System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data}, year={2021}, volume={9}, number={}, pages={117140-117152}, doi={10.1109/ACCESS.2021.3106791}} If you would like to get in touch, please contact an.nguyen@fau.de.

It is a competition on kaggle with stroke Prediction, which is heavily imbalanced.

WikiChurches is a dataset for architectural style classification, consisting of 9,485 images of church buildings. Both images and style labels were sourced from Wikipedia. The dataset can serve as a benchmark for various research fields, as it combines numerous real-world challenges: fine-grained distinctions between classes based on subtle visual features, a comparatively small sample size, a highly imbalanced class distribution, a high variance of viewpoints, and a hierarchical organization of labels, where only some images are labeled at the most precise level. In addition, we provide 631 bounding box annotations of characteristic visual features for 139 churches from four major categories. These annotations can, for example, be useful for research on fine-grained classification, where additional expert knowledge about distinctive object parts is often available.