IEEE Advancing Technology for Humanity;United States: IEEE
摘要:
摘要： Structured continuous-label classification is a variety of classification in which the label is continuous in the data, but the goal is to classify data into classes that are a set of predefined ranges and can be organized in a hierarchy. In the hierarchy, the ranges at the lower levels are more specific and inherently more difficult to predict, whereas the ranges at the upper levels are less specific and inherently easier to predict. Therefore, both prediction specificity and prediction accuracy must be considered when building a decision tree (DT) from this kind of data. This paper proposes a novel classification algorithm for learning DT classifiers from data with structured continuous labels. This approach considers the distribution of labels throughout the hierarchical structure during the construction of trees without requiring discretization in the preprocessing stage. We compared the results of the proposed method with those of the C4.5 algorithm using eight real data sets. The empirical results indicate that the proposed method outperforms the C4.5 algorithm with regard to prediction accuracy, prediction specificity, and computational complexity. 其他題名： TCYB 其他題名： IEEE Trans Cybern 出版者： United States: IEEE 出版日期： 2013-12 出處： IEEE transactions on cybernetics, 2013-12, Vol.43 (6), p.1734-1746 資源來源： IEEE Electronic Library (IEL) 版權： Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Dec 2013 識別號： ISSN: 2168-2267 識別號： ISSN: 2168-2275 識別號： EISSN: 2168-2275 識別號： DOI: 10.1109/TSMCB.2012.2229269 識別號： PMID: 23757571 識別號： CODEN: ITCEB8
