Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning - Special issue on learning with probabilistic representations
Lazy Learning of Bayesian Rules
Machine Learning
Hybrid inductive machine learning: an overview of CLIP algorithms
New learning paradigms in soft computing
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
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
Machine Learning
On Changing Continuous Attributes into Ordered Discrete Attributes
EWSL '91 Proceedings of the European Working Session on Machine Learning
Learning from Inconsistent and Noisy Data: The AQ18 Approach
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Concurrent Discretization of Multiple Attributes
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Comparison of Lazy Bayesian Rule and Tree-Augmented Bayesian Learning
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
Khiops: A Statistical Discretization Method of Continuous Attributes
Machine Learning
CLIP4: hybrid inductive machine learning algorithm that generates inequality rules
Information Sciences: an International Journal - Special issue: Soft computing data mining
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
A Discretization Algorithm Based on a Heterogeneity Criterion
IEEE Transactions on Knowledge and Data Engineering
Toward Unsupervised Correlation Preserving Discretization
IEEE Transactions on Knowledge and Data Engineering
Efficient lazy elimination for averaged one-dependence estimators
ICML '06 Proceedings of the 23rd international conference on Machine learning
ICIT '06 Proceedings of the 9th International Conference on Information Technology
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A Hellinger-based discretization method for numeric attributes in classification learning
Knowledge-Based Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Typicality, Diversity, and Feature Pattern of an Ensemble
IEEE Transactions on Computers
A discretization algorithm based on Class-Attribute Contingency Coefficient
Information Sciences: an International Journal
Top 10 algorithms in data mining
Knowledge and Information Systems
Wrapper discretization by means of estimation of distribution algorithms
Intelligent Data Analysis
Improved Algorithms for Univariate Discretization of Continuous Features
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Weightily averaged one-dependence estimators
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Highly scalable and robust rule learner: performance evaluation and comparison
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid discretization method for naïve Bayesian classifiers
Pattern Recognition
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Current classification problems that concern data sets of large and increasing size require scalable classification algorithms. In this study, we concentrate on several scalable, linear complexity classifiers that include one of the top 10 voted data mining methods, Naïve Bayes (NB), and several recently proposed semi-NB classifiers. These algorithms perform front-end discretization of the continuous features since by design they work only with nominal or discrete features. We address the lack of studies that investigate the benefits and drawbacks of discretization in the context of the subsequent classification. Our comprehensive empirical study considers 12 discretizers (two unsupervised and 10 supervised), seven classifiers (two classical NB and five semi-NB), and 16 data sets. We investigate the scalability of the discretizers and show that the fastest supervised discretizers fast class-attribute interdependency maximization (FCAIM), class-attribute interdependency maximization (CAIM), and information entropy maximization (IEM) provide discretization schemes with the highest overall quality. We show that discretization improves the classification accuracy when compared against the two classical methods, NB and Flexible Naïve Bayes (FNB), executed on the raw data. The choice of the discretization algorithm impacts the significance of the improvements. The MODL, FCAIM, and CAIM methods provide statistically significant improvements, while the IEM, Class-attribute contingency coefficient (CACC), and Khiops discretizers provide moderate improvements. The most accurate classification models are generated by the Averaged one-dependence estimators (AODEsr) classifier followed by AODE and HNB (Hidden Naïve Bayes). AODEsr run on data discretized with MODL, FCAIM, and CAIM provides statistically significantly better accuracies than both the classical NB methods. The worst results are obtained with the NB, FNB, and LBR (Lazy Bayes rule) classifiers. We show that although the time to build the discretization scheme could be longer than the time to train the classifier, the completion of the entire process (to discretize data, compute the classifier, and predict test instances) is often faster than the NB-based classification of the continuous instances. This is because the time to classify test instances is an important factor that is positively influenced by discretization. The biggest positive influence, both on the accuracy and the classification time, is associated with the MODL, FCAIM, and CAIM algorithms.