Visual Integration of Data and Model Space in Ensemble Learning

Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce a workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. We then present a use case in which we start with an ensemble automatically selected by a standard ensemble selection algorithm, and show how we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture

Proceedings of the 15th Conference on Knowledge Organization WissOrg'17 of theGerman Chapter of the International Society for Knowledge Organization (ISKO),30th November - 1st December 2017, Freie Universität Berlin

Wissensorganisation is the name of a series of biennial conferences / workshops with a long tradition, organized by the German chapter of the International Society of Knowledge Organization (ISKO). The 15th conference in this series, held at Freie Universität Berlin, focused on knowledge organization for the digital humanities. Structuring, and interacting with, large data collections has become a major issue in the digital humanities. In these proceedings, various aspects of knowledge organization in the digital humanities are discussed, and the authors of the papers show how projects in the digital humanities deal with knowledge organization.Wissensorganisation ist der Name einer Konferenzreihe mit einer langjährigen Tradition, die von der Deutschen Sektion der International Society of Knowledge Organization (ISKO) organisiert wird. Die 15. Konferenz dieser Reihe, die an der Freien Universität Berlin stattfand, hatte ihren Schwerpunkt im Bereich Wissensorganisation und Digital Humanities. Die Strukturierung von und die Interaktion mit großen Datenmengen ist ein zentrales Thema in den Digital Humanities. In diesem Konferenzband werden verschiedene Aspekte der Wissensorganisation in den Digital Humanities diskutiert, und die Autoren der einzelnen Beiträge zeigen, wie die Digital Humanities mit Wissensorganisation umgehen

Separating Agent-Functioning and Inter-Agent Coordination by Activated Modules: The DECOMAS Architecture

The embedding of self-organizing inter-agent processes in distributed software applications enables the decentralized coordination system elements, solely based on concerted, localized interactions. The separation and encapsulation of the activities that are conceptually related to the coordination, is a crucial concern for systematic development practices in order to prepare the reuse and systematic integration of coordination processes in software systems. Here, we discuss a programming model that is based on the externalization of processes prescriptions and their embedding in Multi-Agent Systems (MAS). One fundamental design concern for a corresponding execution middleware is the minimal-invasive augmentation of the activities that affect coordination. This design challenge is approached by the activation of agent modules. Modules are converted to software elements that reason about and modify their host agent. We discuss and formalize this extension within the context of a generic coordination architecture and exemplify the proposed programming model with the decentralized management of (web) service infrastructures

Of course we share! Testing Assumptions about Social Tagging Systems

Social tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior