English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The amount of stored information in modern database applications increased tremendously in recent years. Besides their sheer amount, the stored data objects are also more and more complex. Therefore, classification of these complex objects is an important data mining task that yields several new challenges. In many applications, the data objects provide multiple representations. E.g. proteins can be described by text, amino acid sequences or 3D structures. Additionally, many real-world applications need to distinguish thousands of classes. Last but not least, many complex objects are not directly expressible by feature vectors. To cope with all these requirements, we introduce a novel approach to classification of multi-represented objects that is capable to distinguish large numbers of classes. Our method is based on k nearest neighbor classification and employs density-based clustering as a new approach to reduce the training instances for instance-based classification. To predict the most likely class, our classifier employs a new method to use several object representations for making accurate class predictions. The introduced method is evaluated by classifying proteins according to the classes of Gene Ontology, one of the most established class systems for biomolecules that comprises several thousand classes.

Multi-represented kNN-Classification for Large Class Sets