The Utility of Knowledge in Inductive Learning
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Dynamic ID3: a symbolic learning algorithm for many-valued attribute domains
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
FILM: a fuzzy inductive learning method for automated knowledge acquisition
Decision Support Systems - Special issue: expertise and modeling expert decision making
Methodological and practical aspects of data mining
Information and Management
Decision Support Systems - Special issue: Decision support systems: Directions for the next decade
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
Incremental Induction of Decision Trees
Machine Learning
Machine Learning
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Compact classification of optimized Boolean reasoning with Particle Swarm Optimization
Intelligent Data Analysis
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The decision tree induction learning approach in machine learning has been extensively applied to the field of knowledge management in practice, since it provides the advantages of quick learning and easy creation of explicit knowledge structures. However, the congenital limitation of node and branch structure may limit the success of decision tree induction learning in dealing with nominal or discrete-valued attributes. The need for discretizing or splitting a candidate continuous-valued attribute into some finite manageable number of intervals therefore is crucial in the application of decision tree induction learning. In this study, an integrated approach was proposed by utilizing the decision tree induction along with the hierarchical clustering analysis which combined the appropriate intervals based on the use of the proposed measure with considerations of both within attribute closeness and discretization results similarity. This proposed integrated approach facilitates the task of multi-interval discretization to produce more accurate classification rules by improving the processing difficulty of continuous-valued attributes for decision tree induction learning. Finally, the proposed approach was tested in terms of both the predictive accuracy and the size of the decision tree by using the UCI-ML testing databases.