Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Machine Learning - Special issue on learning with probabilistic representations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Discriminatively Learning Selective Averaged One-Dependence Estimators Based on Cross-Entropy Method
Computational Intelligence and Security
Survey of Improving Naive Bayes for Classification
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Instance Selection by Border Sampling in Multi-class Domains
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Averaged Naive Bayes Trees: A New Extension of AODE
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
The Knowledge Engineering Review
Scaling up the accuracy of Bayesian classifier based on frequent itemsets by m-estimate
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Flexible learning of k-dependence Bayesian network classifiers
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Improving Tree augmented Naive Bayes for class probability estimation
Knowledge-Based Systems
Cascading customized naïve bayes couple
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Double-layer bayesian classifier ensembles based on frequent itemsets
International Journal of Automation and Computing
Hybrid dynamic k-nearest-neighbour and distance and attribute weighted method for classification
International Journal of Computer Applications in Technology
A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier
Expert Systems with Applications: An International Journal
Boosting for superparent-one-dependence estimators
International Journal of Computing Science and Mathematics
Learning attribute weighted AODE for ROC area ranking
International Journal of Information and Communication Technology
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NB(naive Bayes) is a probabilistic classification model, which is based on the attribute independence assumption. However, in many real-world data mining applications, this assumption is often violated. Responding to this fact, researchers have made a substantial amount of effort to improve NB's accuracy by weakening its attribute independence assumption. For a recent example, Webb et al.[1] propose a model called Averaged One-Dependence Estimators, simply AODE, which weakens the attribute independence assumption by averaging all models from a restricted class of one-dependence classifiers. Motivated by their work, we believe that assigning different weights to these one-dependence classifiers can result in significant improvement. Based on this belief, we present an improved algorithm called Weightily Averaged One-Dependence Estimators, simply WAODE. We experimentally tested our algorithm in Weka system[2], using the whole 36 UCI data sets[3] selected by Weka[2], and compared it to NB, SBC[4], TAN [5], NBTree[6], and AODE[1]. The experimental results show that WAODE significantly outperforms all the other algorithms used to compare.