Analysis of Machine Learning Methods Using Spam Filtering

The paper considers methods of the naive Bayesian classifier. Experiments that show independence between traits are described. Describes the naive Bayesian classifier used to filter spam in messages. The aim of the study is to determine the best method to solve the problem of spam in messages. The paper considers three different variations of the naive Bayesian classifier. The results of experiments and research are given.

An Intention Understanding Algorithm Based on Multimodal Information Fusion

This paper proposes an intention understanding algorithm (KDI) based on an elderly service robot, which combines Neural Network with a seminaive Bayesian classifier to infer user’s intention. KDI algorithm uses CNN to analyze gesture and action information, and YOLOV3 is used for object detection to provide scene information. Then, we enter them into a seminaive Bayesian classifier and set key properties as super parent to enhance its contribution to an intent, realizing intention understanding based on prior knowledge. In addition, we introduce the actual distance between the users and objects and give each object a different purpose to implement intent understanding based on object-user distance. The two methods are combined to enhance the intention understanding. The main contributions of this paper are as follows: (1) an intention reasoning model (KDI) is proposed based on prior knowledge and distance, which combines Neural Network with seminaive Bayesian classifier. (2) A set of robot accompanying systems based on the robot is formed, which is applied in the elderly service scene.

Service Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes

Due to the lack of domain and interface knowledge, it is difficult for users to create suitable service processes according to their needs. Thus, the paper puts forward a new service composition recommendation method. The method is composed of two steps: the first step is service component recommendation based on recurrent neural network (RNN). When a user selects a service component, the RNN algorithm is exploited to recommend other matched services to the user, aiding the completion of a service composition. The second step is service composition recommendation based on Naive Bayes. When the user completes a service composition, considering the diversity of user interests, the Bayesian classifier is used to model their interests, and other service compositions that satisfy the user interests are recommended to the user. Experiments show that the proposed method can accurately recommend relevant service components and service compositions to users.

An AdaBoost-based tree augmented naive Bayesian classifier for transient stability assessment of power systems

By focusing on the accuracy limitations of the naive Bayesian classifier in the transient stability assessment of power systems, a tree augmented naive Bayesian (TAN) classifier is adopted for the power system transient stability assessment. The adaptive Boosting (AdaBoost) algorithm is used in the TAN classifier to form an AdaBoost-based tree augmented naive Bayesian (ATAN) classifier for further classification performance improvement. To construct the ATAN classifier, eight attributes that reasonably reflect the transient stability or transient instability of a power system are selected as inputs of the proposed classifier. In addition, the class-attribute interdependence maximization (CAIM) algorithm is used to discretize the attributes. Then, the operating mechanism of the power system is used to obtain the dependencies between the attributes, and the parameters of the ATAN classifier are learned according to the Bayes’ theorem and the criterion of maximizing a posterior estimation. Four evaluation indicators of the ATAN classifier are used, that is, the value of Kappa, the area under the receiver operating characteristic curve (AUC), F1 score, and the average evaluation indicator. Lastly, experiments are implemented on the IEEE 3-generator 9-bus system and IEEE 10-generator 39-bus system. The simulation results show that the ATAN classifier can significantly improve the classification performance of the transient stability assessment of the power system.

Detection of Intrinsically Resistant Candida in Mixed Samples by MALDI TOF-MS and a Modified Naïve Bayesian Classifier

MALDI-TOF MS is one of the major methods for clinical fungal identification, but it is currently only suitable for pure cultures of isolated strains. However, multiple fungal coinfections might occur in clinical practice. Some fungi involved in coinfection, such as Candida krusei and Candida auris, are intrinsically resistant to certain drugs. Identifying intrinsically resistant fungi from coinfected mixed cultures is extremely important for clinical treatment because different treatment options would be pursued accordingly. In this study, we counted the peaks of various species generated by Bruker Daltonik MALDI Biotyper software and accordingly constructed a modified naïve Bayesian classifier to analyze the presence of C. krusei and C. auris in simulated mixed samples. When reasonable parameters were fixed, the modified naïve Bayesian classifier effectively identified C. krusei and C. auris in the mixed samples (sensitivity 93.52%, specificity 92.5%). Our method not only provides a viable solution for identifying the two highlighted intrinsically resistant Candida species but also provides a case for the use of MALDI-TOF MS for analyzing coinfections of other species.