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Analyzing Multi-Labeled Data Based on the Roll of a Concept against a Semantic Range

Authors: Ke Lu, Tetsuya Furukawa, Masahiro Kuzunishi


Classifying data hierarchically is an efficient approach to analyze data. Data is usually classified into multiple categories, or annotated with a set of labels. To analyze multi-labeled data, such data must be specified by giving a set of labels as a semantic range. There are some certain purposes to analyze data. This paper shows which multi-labeled data should be the target to be analyzed for those purposes, and discusses the role of a label against a set of labels by investigating the change when a label is added to the set of labels. These discussions give the methods for the advanced analysis of multi-labeled data, which are based on the role of a label against a semantic range.

Keywords: Data Analysis, Classification Hierarchies, Orders of Sets of Labels, Multilabeled Data

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