Papers Accepted at IDEAL 2020

We are very pleased to announce that we got two papers accepted for presentatioon at IDEAL 2020 (International Conference on Intelligent Data Engineering and Automated Learning). IDEAL is an annual international conference dedicated to emerging and challenging topics in intelligent data analysis, data mining and their associated learning systems and paradigms. The conference provides a unique opportunity and stimulating forum for presenting and discussing the latest theoretical advances and real-world applications in Computational Intelligence and Intelligent Data Analysis.

Here are the pre-prints of the accepted papers with its abstract:

  • Meta-Hyperband: Hyperparameter optimization with meta-learning and coarse-to-fine
    By Samin Payrosangari, Afshin Sadeghi, Damien Graux, and Jens Lehmann.
    Abstract Hyperparameter optimization is one of the main pillars of machine learning approaches. In this paper, we introduce Meta-Hyperband: a Hyperband based algorithm that improves the search by adding levels of exploitation. Unlike Hyperband which is a pure exploration bandit-based approach for hyperparameter optimization, our meta approach generates a trade-off between exploration and exploitation, combining Hyperband with meta-learning and Coarse-to-Fine modules. We analyze the performance of Meta-Hyperband on various datasets to tune the hyperparameters of CNN and SVM. The experiments indicate that in many cases Meta-Hyperband can discover hyperparameter configurations with higher quality than Hyperband, using similar amounts of resources. In particular, we discovered a CNN configuration for classifying CIFAR10 dataset which has a 3% higher performance than the configuration founded by Hyperband, which is also 0.3% more accurate than the best-reported configuration of the Bayesian optimization approach. Additionally, we release a publicly available pool of historically well-performed configurations on several datasets for CNN and SVM to ease the adoption of Meta-Hyperband.
  • International Data Spaces Information Model – An Ontology for Sovereign Exchange of Digital Content
    By Sebastian Bader, Jaroslav Pullmann, Christian Mader, Sebastian Tramp, Christoph Quix, Andreas Mueller, Haydar Akyürek, Matthias Böckmann, Andreas Mueller, Benedikt Imbusch, Johannes Lipp, Sandra Geisler, and Christoph Lange.
    Abstract The International Data Spaces initiative (IDS) is building an ecosystem to facilitate data exchange in a secure, trusted, and semantically interoperable way. It aims at providing a basis for smart services and cross-company business processes, while at the same time guaranteeing data owners’ sovereignty over their content. The IDS Information Model is an RDFS/OWL ontology defining the fundamental concepts for describing actors in a data space, their interactions, the resources exchanged by them, and data usage restrictions. After introducing the conceptual model and design of the ontology, we explain its implementation on top of standard ontologies as well as the process for its continuous evolution and quality assurance involving a community driven by industry and research organisations. We demonstrate tools that support generation, validation, and usage of instances of the ontology with the focus on data control and protection in a federated ecosystem.