Abstract:Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life machine learning applications. In LDL problems, instance-based algorithms and particularly the adapted version of the k-nearest neighbors method for LDL (AA-kNN) has proven to be very competitive, achieving acceptable results and allowing an explainable model. Howe… Show more
“…Classification with LDL (LDL4C) [21] is another interesting proposition when learned label distribution model is generally treated as a classification model. Feature selection on LDL [36,37] shows promising results by applying selection of characteristics on label distribution problems.…”
Section: Foundations Of Label Distribution Learningmentioning
“…Classification with LDL (LDL4C) [21] is another interesting proposition when learned label distribution model is generally treated as a classification model. Feature selection on LDL [36,37] shows promising results by applying selection of characteristics on label distribution problems.…”
Section: Foundations Of Label Distribution Learningmentioning
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