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Systematic examination of the incorporation of class relationships via multilabel, multiclass, hierarchical classification
Author(s) -
Daisey Katie,
Brown Steven D.
Publication year - 2017
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2885
Subject(s) - class (philosophy) , artificial intelligence , multiclass classification , pattern recognition (psychology) , computer science , machine learning , mathematics , data mining , support vector machine
Difficulties in multiclass classification problems often arise as a result of the transformation to a series of one‐class problems, where each class is determined without respect to the results from the other classes. Existing relationships between the classes themselves, rather than the individual samples in each class, are ignored, and the information is not incorporated into the classification model. Alternatively, classifying multiclass samples via a multilabel, hierarchical classification method can incorporate this information through alternative means of sample selection and comparison. In this paper, the possible methods are systematically examined and the results ranked in terms of two metrics, accuracy and F 1 measure. This method constructed several models that outperformed models constructed by traditional classification methods, showing that incorporating the additional information available in class relationships offers an advantage in performance over traditional “flat” classification.