Scaling up inductive learning with massive parallelism
Machine Learning
Do-I-Care: a collaborative Web agent
Conference Companion on Human Factors in Computing Systems
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
On Relevance, Probabilistic Indexing and Information Retrieval
Journal of the ACM (JACM)
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Lazy Learning of Bayesian Rules
Machine Learning
Machine Learning
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Machine Learning
On Changing Continuous Attributes into Ordered Discrete Attributes
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
Search-Based Class Discretization
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
IEMS - The Intelligent Email Sorter
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Why Discretization Works for Naive Bayesian Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Multi-interval Discretization Methods for Decision Tree Learning
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
BNCOD 14 Proceedings of the 14th British National Conference on Databases: Advances in Databases
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
TAN Classifiers Based on Decomposable Distributions
Machine Learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
OFFD: Optimal Flexible Frequency Discretization for Naïve Bayes Classification
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Information Sciences: an International Journal
Data Mining and Knowledge Discovery
Individual attribute prior setting methods for naïve Bayesian classifiers
Pattern Recognition
Analyzing the impact of the discretization method when comparing Bayesian classifiers
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
An innovative framework for securing unstructured documents
CISIS'11 Proceedings of the 4th international conference on Computational intelligence in security for information systems
A global unsupervised data discretization algorithm based on collective correlation coefficient
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Learning feature-projection based classifiers
Expert Systems with Applications: An International Journal
A hybrid discretization method for naïve Bayesian classifiers
Pattern Recognition
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
Effect of data discretization on the classification accuracy in a high-dimensional framework
International Journal of Intelligent Systems
CD: a coupled discretization algorithm
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
A probabilistic approach to mining geospatial knowledge from social annotations
Proceedings of the 21st ACM international conference on Information and knowledge management
The construction of causal networks to estimate coral bleaching intensity
Environmental Modelling & Software
Improving naive Bayes classifier using conditional probabilities
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Computers & Mathematics with Applications
Data Mining and Knowledge Discovery
Speeding up incremental wrapper feature subset selection with Naive Bayes classifier
Knowledge-Based Systems
Compact classification of optimized Boolean reasoning with Particle Swarm Optimization
Intelligent Data Analysis
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Quantitative attributes are usually discretized in Naive-Bayes learning. We establish simple conditions under which discretization is equivalent to use of the true probability density function during naive-Bayes learning. The use of different discretization techniques can be expected to affect the classification bias and variance of generated naive-Bayes classifiers, effects we name discretization bias and variance. We argue that by properly managing discretization bias and variance, we can effectively reduce naive-Bayes classification error. In particular, we supply insights into managing discretization bias and variance by adjusting the number of intervals and the number of training instances contained in each interval. We accordingly propose proportional discretization and fixed frequency discretization, two efficient unsupervised discretization methods that are able to effectively manage discretization bias and variance. We evaluate our new techniques against four key discretization methods for naive-Bayes classifiers. The experimental results support our theoretical analyses by showing that with statistically significant frequency, naive-Bayes classifiers trained on data discretized by our new methods are able to achieve lower classification error than those trained on data discretized by current established discretization methods.