| dc.description.abstract | Presently, decision tree classifiers are designed to classify the data with categorical or Boolean labels. In many practical situations, however, there are more complex classification scenarios, where the labels to be predicted are not just nominal variable with flat structure. For example, the predicted labels can be (1) hierarchically related, (2) continuous variable, or (3) hierarchical continuous variable. Unfortunately, existing research paid little attention to the issue of classification for constructing a DT from data with various types of labels. To remedy this research gap, this research has developed three innovative label-driven DT algorithms named (1)HLC (Hierarchical Label Classifier), (2)CLC (Continuous Label Classifier), and (3)HCC (Hierarchical Continuous-label Classifier)
HLC, CLC and HCC are different from the traditional decision tree classifiers in some major functions including growing a decision tree, selecting attribute, assigning labels to represent a leaf and making a prediction for a new data. The development strategy of the proposed algorithms is mainly based on measuring similarity among labels by considering data distribution over the predefined concept hierarchy and by a proposed dynamic discretization for the continuous label at each node during the tree-induction process.
The experimental results show that this research can not merely mine classification rules from variety types of labels, but also gets convincing accuracy and precision of rules.
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