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Found 11 papers, 5 papers with code
Ugly Ducklings or Swans: A Tiered Quadruplet Network with Patient-Specific Mining for Improved Skin Lesion Classification
An ugly duckling is an obviously different skin lesion from surrounding lesions of an individual, and the ugly duckling sign is a criterion used to aid in the diagnosis of cutaneous melanoma by differentiating between highly suspicious and benign lesions.
FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systems
This paper presents the FlowTransformer framework, a novel approach for implementing transformer-based Network Intrusion Detection Systems (NIDSs).
NBC-Softmax : Darkweb Author fingerprinting and migration tracking
An author style detection task is a metric learning problem, where learning style features with small intra-class variations and larger inter-class differences is of great importance to achieve better performance.
Efficient block contrastive learning via parameter-free meta-node approximation
In this work, we propose a meta-node based approximation technique that can (a) proxy all negative combinations (b) in quadratic cluster size time complexity, (c) at graph level, not node level, and (d) exploit graph sparsity.
Empirical study of Machine Learning Classifier Evaluation Metrics behavior in Massively Imbalanced and Noisy data
In this work, we develop a theoretical foundation to model human annotation errors and extreme imbalance typical in real world fraud detection data sets.
Credit card fraud detection - Classifier selection strategy
Machine learning has opened up new tools for financial fraud detection.
Challenges and Complexities in Machine Learning based Credit Card Fraud Detection
Given past transactions, a machine learning algorithm has the ability to 'learn' infinitely complex characteristics in order to identify frauds in real-time, surpassing the best human investigators.
XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics
The proposed model comprises a botnet detector and an explainer for automatic forensics.
SCGC : Self-Supervised Contrastive Graph Clustering
We also propose SCGC*, with a more effective, novel, Influence Augmented Contrastive (IAC) loss to fuse richer structural information, and half the original model parameters.
Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin
The proposed method was evaluated on the Elliptic dataset and shows that our approach outperforms the state-of-the-art in terms of key classification metrics, which demonstrates the potential of self-supervised GNN in the detection of illicit cryptocurrency transactions.
FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping
While Graph Neural Networks have gained popularity in multiple domains, graph-structured input remains a major challenge due to (a) over-smoothing, (b) noisy neighbours (heterophily), and (c) the suspended animation problem.
Ranked #3 on
Node Classification
on Pubmed Full-supervised
