anomaly-detection-resources
TabFormer
| anomaly-detection-resources | TabFormer | |
|---|---|---|
| 98 | 10 | |
| 7,887 | 296 | |
| - | 2.4% | |
| 4.6 | 0.0 | |
| 13 days ago | 9 months ago | |
| Python | Python | |
| GNU Affero General Public License v3.0 | Apache License 2.0 |
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anomaly-detection-resources
- anomaly-detection-resources: NEW Extended Research - star count:7507.0
- anomaly-detection-resources: NEW Extended Research - star count:7323.0
- anomaly-detection-resources: NEW Extended Research - star count:7109.0
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Time-based splitting performing significantly worse than random splitting
https://github.com/yzhao062/anomaly-detection-resources https://search.brave.com/search?q=imbalanced+dataset+machine+learning+github&source=desktop
TabFormer
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Time-based splitting performing significantly worse than random splitting
Hi, I am currently working on a basic binary classifier for a transaction dataset, to predict which transaction is fraudulent (Dataset: https://github.com/IBM/TabFormer). The following is a quick summary of the dataset:
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Question regarding Relational Graph Convolutional Network for a Fraud Detection problem
I am currently working on a transaction dataset (https://github.com/IBM/TabFormer/tree/main/data/credit_card) and I intend to build a fraud detection engine, but with tabular data transformed into a graph. I have used this article as my main outline for this approach: https://developer.nvidia.com/blog/optimizing-fraud-detection-in-financial-services-with-graph-neural-networks-and-nvidia-gpus/.
- TabFormer: NEW Data - star count:231.0
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[D] Neural Networks are not the only universal approximators, so why are they so uniquely effective?
When people talk about tabular data they mean something with like <100 columns where your classification might strongly depend on a handful of specific ones. There is of course a regime where data is "somewhat" tabular (some NLP problems) so it's not entirely well-defined. And there are NN architecture for tabular data like the tabformer.
What are some alternatives?
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Transformers4Rec - Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.
pygod - A Python Library for Graph Outlier Detection (Anomaly Detection)
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
loglizer - A machine learning toolkit for log-based anomaly detection [ISSRE'16]
bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
pycaret - An open-source, low-code machine learning library in Python
quickai - QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
zeroshot_topics - Topic Inference with Zeroshot models
DGFraud - A Deep Graph-based Toolbox for Fraud Detection