Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka Conference p

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LSCALE: Latent Space Clustering-Based Active Learning for Node Classifications. Specifically, to select nodes for labelling, our framework uses the K-Medoids clustering algorithm on a latent space based on a dynamic combination of both unsupervised features and supervised features. In addition, we design an incremental clustering module to avoid redundancy between nodes sele

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Powershap: A Power-Full Shapley Feature Selection Method intuitive feature selection. . is built on the core assumption that an informative feature will have a larger impact on the prediction compared to a known random feature. Benchmarks and simulations show that . outperforms other filter methods with predictive performances on par with wrapper methods

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刚毅

Nonparametric Bayesian Deep Visualizationed to eliminate the necessity to optimize weights and layer widths. Additionally, to determine latent dimensions and the number of clusters without tuning, we propose a latent variable model that combines NNGP with automatic relevance determination [.] to extract necessary dimensions of latent space

anticipate

FastDEC: Clustering by Fast Dominance Estimationrobust, and .-NN based variant of the classical density-based clustering algorithm: Density Peak Clustering (DPC). DPC estimates the significance of data points from the density and geometric distance factors, while FastDEC innovatively uses the global rank of the dominator as an additional factor i

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SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting where the clusters themselves evolve with an evolving stream. Using real and synthetic datasets, we empirically demonstrate that SECLEDS produces high-quality clusters regardless of drift, stream size, data dimensionality, and number of clusters. We compare against three popular stream and batch cl

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Hop-Count Based Self-supervised Anomaly Detection on Attributed Networkstuition, we propose a hop-count based model (HCM) that achieves that. Our approach includes two important learning components: (1) Self-supervised learning task of predicting the shortest path length between a pair of nodes, and (2) Bayesian learning to train HCM for capturing uncertainty in learned