Statistics: principles and methods
Statistics: principles and methods
Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Why Discretization Works for Naive Bayesian Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
OFFD: Optimal Flexible Frequency Discretization for Naïve Bayes Classification
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Combining Feature Selection and Local Modelling in the KDD Cup 99 Dataset
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Local modeling classifier for microarray gene-expression data
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Expert Systems with Applications: An International Journal
A nearest features classifier using a self-organizing map for memory base evaluation
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Unsupervised discretization using tree-based density estimation
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Incremental discretization for Naïve-Bayes classifier
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
International Journal of Business Intelligence and Data Mining
Non-Disjoint discretization for aggregating one-dependence estimator classifiers
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier
Expert Systems with Applications: An International Journal
A decision-making model for environmental behavior in agent-based modeling
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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This paper argues that two commonly-used discretization approaches, fixed k-interval discretization and entropy-based discretization have sub-optimal characteristics for naive-Bayes classification. This analysis leads to a new discretization method, Proportional k-Interval Discretization (PKID), which adjusts the number and size of discretized intervals to the number of training instances, thus seeks an appropriate trade-off between the bias and variance of the probability estimation for naive-Bayes classifiers. We justify PKID in theory, as well as test it on a wide cross-section of datasets. Our experimental results suggest that in comparison to its alternatives, PKID provides naive-Bayes classifiers competitive classification performance for smaller datasets and better classification performance for larger datasets.