A Mobile Bayesian Network Structure Learning Method Using Genetic Incremental K2 Algorithm and Random Attribute Order Technology

The application of existing datasets to construct a probabilistic network has always been the primary research focus for mobile Bayesian networks, particularly when the dataset size is large. In this study, we improve the K2 algorithm. First, we relax the K2 algorithm requirements for node order and generate the node order randomly to obtain the best result in multiple random node order. Second, a genetic incremental K2 learning method is used to learn the Bayesian network structure. The training dataset is divided into two groups, and the standard K2 algorithm is used to find the optimal value for the first set of training data; simultaneously, three similar suboptimal values are recorded. To avoid falling into the local optimum, these four optimal values are mutated into a new genetic optimal value. When the second set of training data is used, only the best Bayesian network structure within the five abovementioned optimal values is identified. The experimental results indicate that the genetic incremental K2 algorithm based on random attribute order achieves higher computational efficiency and accuracy than the standard algorithm. The new algorithm is especially suitable for building Bayesian network structures in cases where the dataset and number of nodes are large.

Multivariate Data Quality Enhancement by Ranked Imputation

Organizational decisions are based on data-based-analysis and predictions. Effective decisions require accurate predictions, which in-turn depend on the quality of the data. Real time data is prone to inconsistencies, which exhibit negative impacts on the quality of the predictions. This mandates the need for data imputation techniques. This work presents a prediction-based data imputation technique, Rank Based Multivariate Imputation (RBMI) that operates on multivariate data. The proposed model is composed of the ranking phase and the imputation phase. Ranking dictates, the attribute order in which imputation is to be performed. The proposed model utilizes tree-based approach for the actual imputation process. Experiments were performed on Pima, a diabetes dataset. The data was amputed in range between 5% - 30%. The obtained results were compared with existing state-of-the-art models in terms of MAE and MSE levels. The proposed RBMI model exhibits a reduction of 0.03 in MAE levels and 0.001 in MSE levels.

A Weighted Naive Bayes Algorithm Based on the Attribute Order Reduction

Naïve Bayes classifier was generally considered as a simple and efficient classification method. However, its classification performance was affected to some extent because of the assuming that the conditions properties were independent of each other. By analyzing the classification principle and improvement of Bayesian and the Attribute Reduction of Rough Set, this paper proposed a Naïve Bayes algorithm that the attribute order reduction and weighting were improved simultaneously. Experiment results demonstrated that the proposed method performed well in classification accuracy.