This paper describes novel and practical Japanese parsers that uses decision trees. First, we construct a single decision tree to estimate modification probabilities; how one phra...
Decision trees are one of the most easy to use tools in decision analysis. Problems where decision tree branches are based on random variables have not received much attention. Th...
Mohamed Moussa, Janaka Y. Ruwanpura, George Jergea...
We introduce a new genetic operator, Reduction, that rectifies decision trees not correct syntactically and at the same time removes the redundant sections within, while preservin...
Artificial Neural Networks (ANNs) have proved both a popular and powerful technique for pattern recognition tasks in a number of problem domains. However, the adoption of ANNs in ...
Most decision tree induction methods used for extracting knowledge in classification problems are unable to deal with uncertainties embedded within the data, associated with human...
In this paper we report our work on building a POS tagger for a morphologically rich language- Hindi. The theme of the research is to vindicate the stand that- if morphology is st...
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
Decision trees are among the most popular pattern types in data mining due to their intuitive representation. However, little attention has been given on the definition of measure...
Using decision trees that split on randomly selected attributes is one way to increase the diversity within an ensemble of decision trees. Another approach increases diversity by ...
Michael Gashler, Christophe G. Giraud-Carrier, Ton...
We propose a new decision tree algorithm, Class Confidence Proportion Decision Tree (CCPDT), which is robust and insensitive to class distribution and generates rules which are st...
Wei Liu, Sanjay Chawla, David A. Cieslak, Nitesh V...