TY - JOUR
T1 - A novel decision-tree method for structured continuous-label classification
AU - Hu, Hsiao Wei
AU - Chen, Yen Liang
AU - Tang, Kwei
PY - 2013/12
Y1 - 2013/12
N2 - 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.
AB - 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.
KW - Classification algorithms
KW - Data mining
KW - Decision trees (DTs)
UR - http://www.scopus.com/inward/record.url?scp=84890080043&partnerID=8YFLogxK
U2 - 10.1109/TSMCB.2012.2229269
DO - 10.1109/TSMCB.2012.2229269
M3 - 期刊論文
C2 - 23757571
AN - SCOPUS:84890080043
SN - 2168-2267
VL - 43
SP - 1734
EP - 1746
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 6
M1 - 6418009
ER -