Improvement Research And Application Of Bayesian Classification Based On Different Scenes

Bayesian classification algorithm is widely used in practical applications.However,in the application of mobile user behavior classification,which requires high timeliness and classification accuracy,the existing Bayesian classification algorithm is not very satisfactory.The Naive Bayesian Classification(NBC)algorithm with high computational efficiency and simple algorithm structure is one of the Bayesian classical classification algorithms.However,the assumption that the NBC algorithm is independent of data attributes limits the application of NBC.The Hidden Naive Bayesian(HNB)classification algorithm is an improved algorithm based on the NBC algorithm and considers the degree of association between data attributes into the calculation of probability.Then the classification accuracy of all algorithms can be effectively improved.However,the degree of association between the data attributes considered by the HNB algorithm is too complicated,and it is difficult to achieve the scene requirements desiring high classification and timeliness in practical applications.The Average Single Dependency Estimation algorithm(AODE)is also an improved algorithm based on the NBC algorithm,which weakens the assumptions of the NBC algorithm that are independent of each other.However,the algorithm only considers the influence of a non-class parent attribute,so the degree of association between attributes is not considered enough.Therefore,this shortcoming affects the accuracy of the classification of the AODE algorithm to a certain extent,making the algorithm difficult to meet the demands in scenarios requiring high accuracy.In view of the the problem that the HNB is difficult to be applied to the scenes with high timeliness,considering the HNB classification algorithm is complex and high,and the classification timeliness is relatively low.Hidden Naive Bayesian Classification based on Important Parent Attributes(HNB-IPA)is proposed by this paper,which uses the algorithm CFS to select a set of parent attributes that are more important for each attribute,and the selected set of important parent attributes is used to improve the probability calculation of the HNB algorithm.Finally,the HNB-IPA algorithm performs simulation experiments on multiple sets of data set on UCI.The experimental results show that HNB-IPA algorithm can greatly improve the timeliness of classification under the condition of lesser classificationaccuracy and meet the need of classifing scenes with high timeliness.In view of the lack of relevance of AODE to data attributes,affecting classification accuracy,it is difficult to be applied to scenarios requiring high classification accuracy.This paper proposes an Average Single Dependency Estimation based on secondary parent attribute by introducing the concept of secondary parent attribute nodes.Average Single Dependency Estimation algorithm(AODE-SPA),which combines the effects of other attributes on specific attributes into the secondary parent attribute node,thus making the relevance of the data attributes more comprehensive.Finally,the AODE-SPA classification algorithm performs simulation experiments on multiple sets of data set on UCI.The experimental results show that the AODE-SPA algorithm can improve the classification accuracy to a certain extent and greatly meet the requirements of scenes with high accuracy requirements.Finally,this paper preprocesses certain online logs of mobile users in some areas of Changchun for a period of time,and applies the classification algorithms NBC,HNB and HNB-IPA to the behavior classification of mobile users in the scenario where the classification timeliness is required.Experimental results show that the classification time of the HNB-IPA algorithm is only 33% of the HNB algorithm,and the classification accuracy is only 0.36% lower than the HNB algorithm.In the scenario where the classification accuracy is high,the classification algorithms NBC,C4.5,AODE,HNB and AODE-SPA are used in the behavior classification of mobile users.The experimental results show that the classification accuracy of AODE-SPA classification algorithm is 76.49% higher than other classification algorithms.This indicates that the two classification algorithms HNB-IPA and AODE-SPA proposed in this paper can be applied to mobile user behavior classification prediction under two scenarios requiring high timeliness and classification accuracy.