By Michael R. Berthold (auth.), João Gama, Elizabeth Bradley, Jaakko Hollmén (eds.)
This ebook constitutes the refereed lawsuits of the tenth foreign convention on clever information research, IDA 2011, held in Porto, Portugal, in October 2011. the nineteen revised complete papers and sixteen revised poster papers resented including three invited papers have been conscientiously reviewed and chosen from seventy three submissions. All present points of clever facts research are addressed, quite clever aid for modeling and interpreting advanced, dynamical structures. The papers supply clever help for figuring out evolving medical and social structures together with information assortment and acquisition, reminiscent of crowd sourcing; information cleansing, semantics and markup; looking for information and assembling datasets from a number of assets; information processing, together with workflows, mixed-initiative info research, and making plans; facts and data fusion; incremental, mixed-initiative version improvement, checking out and revision; and visualization and dissemination of effects; etc.
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Additional info for Advances in Intelligent Data Analysis X: 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings
The model md returns the class label of an unlabelled record x, such that md (x) = y ∈ Y . , the number of predictions diﬀerent from fd ). However, the underlying concept of interest fd may change over time and the number of labelled records available for that concept are sometimes limited. To address such situations, we propose to exploit models from the community and use the available labelled records from DSd to obtain a model md . We expect md to be more accurate than using the local labelled records alone when building the model.
For example, in a stream of documents where each word is a feature, it is impossible to know in advance which words will appear over time, and thus what is the best feature space to represent the documents with. Using a very large vocabulary of words results ineﬃcient, as most of the words will likely be redundant and only a small subset of words is ﬁnally useful for classiﬁcation. Over time, it is also likely that new important features appear and that previously selected features become less important, which brings change to the subset of relevant features.
1 Problem Definition This work is focused on collaborative data stream mining between ubiquitous devices. Each ubiquitous device d aims to learn the underlying concept from a local stream DSd of labelled records where the set of class labels Y is ﬁxed. However, the feature space X is not static. Let Xdi ⊆ X be the feature space for DSd and its ith record Xi = (xi , yi ) with xi ∈ Xdi and yi ∈ Y . We assume that the underlying concept is a function fd that assigns each record xi to the true class label yi .