Attribute weighted Naive Bayes classifier using a local optimization
Double bundle method for finding clarke stationary points in nonsmooth dc programming
Globally convergent algorithms for solving unconstrained optimization problems
Improving Naive Bayes classifier using conditional probabilities
Learning the naive bayes classifier with optimization models
In this paper a new method of preprocessing incomplete data is introduced. The method is based on clusterwise linear regression and it combines two well-known approaches for missing value imputation: linear regression and clustering. The idea is to approximate missing values using only those data points that are somewhat similar to the incomplete data point. A similar idea is used also in clustering based imputation methods. Nevertheless, here the linear regression approach is used within each cluster to accurately predict the missing values, and this is done simultaneously to clustering. The proposed method is tested using some synthetic and real-world data sets and compared with other algorithms for missing value imputations. Numerical results demonstrate that the proposed method produces the most accurate imputations in MCAR and MAR data sets with a clear structure and the percentages of missing data no more than 25%
Multi-source cyber-attacks detection using machine learning
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