Lazy Learning of Bayesian Rules
Machine Learning
Effective Methods for Improving Naive Bayes Text Classifiers
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment
Applied Soft Computing
Fast solvers and efficient implementations for distance metric learning
Proceedings of the 25th international conference on Machine learning
ICINIS '08 Proceedings of the 2008 First International Conference on Intelligent Networks and Intelligent Systems
Solving TSP with Novel Local Search Heuristic Genetic Algorithms
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A genetic algorithm for classification
ICCC'11 Proceedings of the 2011 international conference on Computers and computing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Bayesian forecaster using class-based optimization
Applied Intelligence
The decomposed k-nearest neighbor algorithm for imbalanced text classification
FGIT'12 Proceedings of the 4th international conference on Future Generation Information Technology
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
k Nearest neighbor, Bayesian methods and genetic algorithms are effective methods of machine learning. In this work a hybrid method is formed by using these methods and algorithm together. The aim is to achieve successful results on classifying by eliminating data that make difficult to learn. Forming new data set approach is proposed according to good data on the hand. Test process is done with five of UCI machine learning datasets. These are iris, breast cancer, glass, yeast and wine data sets. Test results are investigated in collaboration with the previous works, and the success of the study is considered.