Decision tree induction has been widely used to generate classifiers from training data through a process of recursively splitting the data space. In the case of training on continuous-valued data, the associated attributes must be discretized in advance or during the learning process. The commonly ...
Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mi... Groundwater is an important source to supply drinking water demands in both arid and semi-arid regions. Nevertheless, locating high qualit...
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aMany of the recent proposals for inductive databases and constraint-based data mining are restricted to single pattern domains (such as itemsets or molecular fragments) or single tasks, such as pattern discovery or decision tree induction. 许多最近提议对于引人数据库和基于限制的数据采集被制约选拔样式...
The minimum number of realizable nodes in such decision trees is equal to 2 n + 1 , the minimum number of working nodes is equal to n, and the minimum number of realizable terminal nodes is equal to n + 1 . However, the minimum depth of a decision tree solving this problem using ...
Most decision tree induction algorithms have a top-down approach; the decision tree is recursively built by dividing the training data into smaller subsets. The representation of knowledge learned in the shape of a tree is intuitive and usually easy for humans to assimilate; in addition, decision...
Additionally, for many reasons, including model validation and attendance to new legislation, there is an increasing interest in interpretable models, such as those created by the decision tree (DT) induction algorithms. This paper provides a comprehensive approach for investigating the effects of ...
This work suggests a guideline to use systematic accuracy, coherence, and tree analyses in selecting an optimal DT model from multiple candidate model variants, and demonstrates the applicability of the OPP-improved DT integrated with GIS in rule induction for mapping bacterial diversity. These ...
Decision trees are an important data mining tool with many applications. Like many classification techniques, decision trees process the entire data base in order to produce a generalization of the data that can be used subsequently for classification. Large, complex data bases are not always ...
C., and Pullela, S. V. V. S. R. K. A study of decision tree induction for data stream mining using boosting genetic programming classifier. In Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I. pp. 315-322, 2011....