C4.5: programs for machine learning
C4.5: programs for machine learning
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
A Modified Chi2 Algorithm for Discretization
IEEE Transactions on Knowledge and Data Engineering
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
An Extended Chi2 Algorithm for Discretization of Real Value Attributes
IEEE Transactions on Knowledge and Data Engineering
A Discretization Algorithm Based on a Heterogeneity Criterion
IEEE Transactions on Knowledge and Data Engineering
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A discretization algorithm based on Class-Attribute Contingency Coefficient
Information Sciences: an International Journal
Utilization of Discretization method on the diagnosis of optic nerve disease
Computer Methods and Programs in Biomedicine
A non-parametric semi-supervised discretization method
Knowledge and Information Systems
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Improved full-discretization method for milling chatter stability prediction with multiple delays
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part II
ICICTA '11 Proceedings of the 2011 Fourth International Conference on Intelligent Computation Technology and Automation - Volume 01
EDISC: A Class-Tailored Discretization Technique for Rule-Based Classification
IEEE Transactions on Knowledge and Data Engineering
A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning
IEEE Transactions on Knowledge and Data Engineering
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Discretization is a process to convert continuous attributes into discrete format to represent signals for further data processing in learning systems. The main concern in discretization techniques is to find an optimal representation of continuous values with limited number of intervals that can effectively characterize the data and meanwhile minimize information loss. In this paper, we propose a novel class-attribute interdependency discretization algorithm (termed as NCAIC), which takes account of data distribution and the interdependency between all classes and attributes. In our proposed solution, the upper approximation of rough sets as a prime part of the discretization algorithm is applied, and the class-attribute mutual information is used to automatically control and adjust the scope of the discretization of continuous attributes. Some experiments with comparison to five other discretization algorithms are reported, where 13 benchmarked datasets extracted from UCI database and the well-known C4.5 decision tree tool are employed in this study. Results demonstrate that in general our proposed algorithm outperforms other tested discretization algorithms in terms of classification performance.