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Extra resources for Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part III
The value of batch size, b was determined depending on the total number of nodes in the network. For multi-labeled networks we use hamming loss (lower the better) and micro-F1 score (higher the better) as evaluation metrics [7,15]. For each round of active learning using FLIP, we choose the batchsize b = 5 for IMDB-actor, b = 20 for DBLP(B) and b = 30 for DBLP(A) networks, respectively. For FLIP-per-label, b = 110 for IMDBactor, b = 60 for DBLP(B) and b = 90 for DBLP(A) are used. We conducted 30 and 50 rounds of active learning for single-labeled and multi-labeled networks, respectively, in order to observe the convergence of all the comparing methods.
We propose a method called FLIP-per-label for pool-based active learning to address this real-world situation. Our contributions in this paper are summarized as follows: 1. Active learning strategy for single labeled and multi-labeled networks (FLIP) 2. Active learning strategy for querying a subset of labels of an instance for multi-labeled network (FLIP-per-label) These methods were developed assuming no node features are available to us during learning. We experimented with six real world single-labeled and three multi-labeled networks, and our results show statistically signiﬁcant improvements over random sampling and other baselines for most of the datasets.
Matteo Riondato 485 489 494 499 504 508 512 516 Table of Contents – Part III Heterogeneous Stream Processing and Crowdsourcing for Traﬃc Monitoring: Highlights . . . . . . . . . . . . . . . . . . . . . . Fran¸cois Schnitzler, Alexander Artikis, Matthias Weidlich, Ioannis Boutsis, Thomas Liebig, Nico Piatkowski, Christian Bockermann, Katharina Morik, Vana Kalogeraki, Jakub Marecek, Avigdor Gal, Shie Mannor, Dermot Kinane, and Dimitrios Gunopulos XXXV 520 Agents Teaching Agents in Reinforcement Learning (Nectar Abstract) .