Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
On changing continuous attributes into ordered discrete attributes
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Dynamic Programming
Multivariate discretization for set mining
Knowledge and Information Systems
Feature Selection via 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
Machine Learning
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Induction of Recursive Bayesian Classifiers
ECML '93 Proceedings of the European Conference on Machine Learning
Class-Driven Statistical Discretization of Continuous Attributes (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Fuzzy Approach to Partitioning Continuous Attributes for Classification
IEEE Transactions on Knowledge and Data Engineering
Wrapper discretization by means of estimation of distribution algorithms
Intelligent Data Analysis
Pattern discovery for large mixed-mode database
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
EDLRT: Entropy-based dummy variables logistic regression tree
Intelligent Data Analysis
Gene selection based on mutual information for the classification of multi-class cancer
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Gene selection by cooperative competition clustering
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
UniDis: a universal discretization technique
Journal of Intelligent Information Systems
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This paper presents a new method to convert continuous variables into discrete variables for inductive machine learning. The method can be applied to pattern classification problems in machine learning and data mining. The discretization process is formulated as an optimization problem. We first use the normalized mutual information that measures the interdependence between the class labels and the variable to be discretized as the objective function, and then use fractional programming (iterative dynamic programming) to find its optimum. Unlike the majority of class-dependent discretization methods in the literature which only find the local optimum of the objective functions, the proposed method, OCDD, or Optimal Class-Dependent Discretization, finds the global optimum. The experimental results demonstrate that this algorithm is very effective in classification when coupled with popular learning systems such as C4.5 decision trees and Naive-Bayes classifier. It can be used to discretize continuous variables for many existing inductive learning systems.