LAUSR

Outlier detection is a significant research direction in machine learning and has many applications in finance, network security, and other areas. Outlier detection of Euclidean datasets is a mainstream problem in outlier detection. Most detection methods often ignore the connection of its nodes. To collect the representation information of feature sets and node connections to improve the detection of outliers in Euclidean datasets Accuracy rate, we propose a novel Graph Convolutional and Attention-Based Outlier Detection (GCA).The GCA first converts the Euclidean structure data into directed graphs using locally sensitive hashing; then, by applying a Graph Convolutional Network, the data features and their connectivity graph are fed into the neural network; secondly, it fuses the extracted features and the features reconstructed by the attention mechanism; finally, calculating the outlier factors of the objects. Comparing eight state-of-art algorithms on ten real-world datasets shows that GCA achieves the highest Area Under ROC Curve (AUC) on datasets and also achieves equally good results in Accuracy (ACC) and False Alarm Rate (FAR). This study fills the gap of upgraded GCNs in detecting outliers to the best of our knowledge and provides a new way to convert Euclidean data to graphs.