The dataset is created to addressing a real-world challenge: creating a clean fine-grained taxonomy dataset.

We present the Noisy Ostracods, a noisy dataset for genus and species classification of crustacean ostracods with specialists' annotations. Over the 71466 specimens collected, 5.58% of them are estimated to be noisy (possibly problematic) at genus level. The dataset is created to addressing a real-world challenge: creating a clean fine-grained taxonomy dataset. The Noisy Ostracods dataset has diverse noises from multiple sources. Firstly, the noise is open-set, including new classes discovered during curation that were not part of the original annotation. The dataset has pseudo-classes, where annotators misclassified samples that should belong to an existing class into a new pseudo-class. The Noisy Ostracods dataset is highly imbalanced with a imbalance factor $\rho$ = 22429. This presents a unique challenge for robust machine learning methods, as existing approaches have not been extensively evaluated on fine-grained classification tasks with such diverse real-world noise. Initial experiments using current robust learning techniques have not yielded significant performance improvements on the Noisy Ostracods dataset compared to cross-entropy training on the raw, noisy data. On the other hand, noise detection methods have underperformed in error hit rate compared to naive cross-validation ensembling for identifying problematic labels. These findings suggest that the fine-grained, imbalanced nature, and complex noise characteristics of the dataset present considerable challenges for existing noise-robust algorithms. By openly releasing the Noisy Ostracods dataset, our goal is to encourage further research into the development of noise-resilient machine learning methods capable of effectively handling diverse, real-world noise in fine-grained classification tasks. The dataset, along with its evaluation protocols, can be accessed at https://github.com/H-Jamieu/Noisy_ostracods.

The use of training, validation and test datasets is common but not easily understood. In this post, I attempt to clarify this concept. The post is part of my forthcoming book on learning Artificial Intelligence, Machine Learning and Deep Learning based on high school maths. And then comes up with an important statement: Reference to a "validation dataset" disappears if the practitioner is choosing to tune model hyperparameters using k-fold cross-validation with the training dataset. Model selection: involves selecting optimal parameters or a model.

In this special guest feature, Arjun Kakkar, Vice President Strategy and Operations at Ekata, provides 9 practical and actionable principles for product managers and business leaders working to use machine learning for fraud detection. Arjun works with Ekata's operating teams to drive customer value across e-commerce, payments, marketplaces and online lending verticals. The total recorded cost of global online fraud is about $25 billion [1]. But the real value is at least 20 times higher, because, to catch fraud, online merchants and banks often mistakenly reject legitimate customers. This blunder represents at least $500 billion in lost lifetime revenue for online commerce, not to mention a priceless amount of customer trust.

The use of training, validation and test datasets is common but not easily understood. In this post, I attempt to clarify this concept. The post is part of my forthcoming book on learning Artificial Intelligence, Machine Learning and Deep Learning based on high school maths. And then comes up with an important statement: Reference to a "validation dataset" disappears if the practitioner is choosing to tune model hyperparameters using k-fold cross-validation with the training dataset. Model selection: involves selecting optimal parameters or a model.