The Experts below are selected from a list of 39621 Experts worldwide ranked by ideXlab platform
Xiang-hui Zhao - One of the best experts on this subject based on the ideXlab platform.
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Spam Recognition Based on Bayesian Classification
DEStech Transactions on Computer Science and Engineering, 2017Co-Authors: Yang-ping Zhang, Xiang-hui ZhaoAbstract:Based on Bayesian Classification algorithm principle and implementation, propose an improved method of the algorithm. Firstly, instead of constant probability of spam, actual priori probability is used. Secondly, the selective range and rule of token is improved. Finally, add URLs and images into detection content. A mail recognizer based on improved Bayesian Classification is designed. The experiment result shows that the improved Bayesian Classification algorithm works well in practice.
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Spam Detection Utilizing Statistical-Based Bayesian Classification
Proceedings of the 2016 International Conference on Applied Mathematics Simulation and Modelling, 2016Co-Authors: Xiang-hui Zhao, Yang-ping ZhangAbstract:Spam is one of the major problem of today's life because it causes a lot of extra expense both in network infrastructure and our individual life. Among those approaches developed to detect spam, the content-based detection technique, especially statistical-based Bayesian algorithm is important and popular. However, the basic Bayesian algorithm permits on assumption and estimation. In this paper, we proposed an improved method to increase the accuracy of the algorithm. Firstly, use actual priori probability instead of constant probability of spam. Secondly, expand the selective range and rule of tokens. Finally, add URLs and images into detection content. Keywords-spam; statistical-based Bayesian Classification; content detection
Jialin Liu - One of the best experts on this subject based on the ideXlab platform.
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Fault Detection and Identification Using Modified Bayesian Classification on PCA Subspace
Industrial & Engineering Chemistry Research, 2009Co-Authors: Jialin Liu, Ding-sou ChenAbstract:A novel process monitoring method based on modified Bayesian Classification on PCA subspace is proposed. Fault detection and identification are the major steps to diagnose root causes of a process fault. However, before the faulty variables from the abnormal operations are identified, the different operating states need to be clustered from the historical data. The proposed approach modifies the Bayesian Classification method to cluster data into groups. Therefore, a new fault identification index is derived based on cluster center and covariance. An industrial compressor process is used to demonstrate the effectiveness of the proposed approach. In the example, process-insight-based variables were monitored along with the measured variables. The capability of fault diagnosis has been improved, since the fault identification indices are directly related to the variables with process characteristics.
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High-Pressure Polyethylene Process Monitoring Using PCA Based Bayesian Classification
IFAC Proceedings Volumes, 2004Co-Authors: Jialin LiuAbstract:Abstract Using PCA based Bayesian Classification to monitor the real plant with different operated conditions is proposed. Since the process condition s are time-variant, as the PCA subspace cannot explain the data of new events. the PCA should be reperformed. In this work the method of updating Bayesian model is developed. Only the data of new events are trained in the newer subspace. The ability of PCA based Bayesian Classification for monitoring different operating conditions is demonstrated using the real daIa mm high-pressure polyethylene plant.
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Process Monitoring Using Bayesian Classification on PCA Subspace
Industrial & Engineering Chemistry Research, 2004Co-Authors: Jialin LiuAbstract:A new approach to fault detection and isolation that combines principal component analysis (PCA) and Bayesian Classification is proposed. For a given set of training data, a PCA subspace is constructed, and the score vectors are classified by solving the Bayesian Classification problem with the deterministic annealing expectation maximization (DAEM) and robust mixture decomposition (RMD) algorithms. If data for new events do not belong to the existing subspace, the PCA subspace must be rebuilt to include the new events. Whereas new data form their own classes in the new subspace, existing Classifications of the old data must also be updated in the newer subspace. This can be done using a simple translation and rotation if the dimensionality of the new subspace is the same as that of the old subspace. If the new subspace is of higher dimension, a good initial estimate is generated using translation and rotation. This estimate ensures convergence within a few expectation maximization steps. The proposed met...
Yang-ping Zhang - One of the best experts on this subject based on the ideXlab platform.
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Spam Recognition Based on Bayesian Classification
DEStech Transactions on Computer Science and Engineering, 2017Co-Authors: Yang-ping Zhang, Xiang-hui ZhaoAbstract:Based on Bayesian Classification algorithm principle and implementation, propose an improved method of the algorithm. Firstly, instead of constant probability of spam, actual priori probability is used. Secondly, the selective range and rule of token is improved. Finally, add URLs and images into detection content. A mail recognizer based on improved Bayesian Classification is designed. The experiment result shows that the improved Bayesian Classification algorithm works well in practice.
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Spam Detection Utilizing Statistical-Based Bayesian Classification
Proceedings of the 2016 International Conference on Applied Mathematics Simulation and Modelling, 2016Co-Authors: Xiang-hui Zhao, Yang-ping ZhangAbstract:Spam is one of the major problem of today's life because it causes a lot of extra expense both in network infrastructure and our individual life. Among those approaches developed to detect spam, the content-based detection technique, especially statistical-based Bayesian algorithm is important and popular. However, the basic Bayesian algorithm permits on assumption and estimation. In this paper, we proposed an improved method to increase the accuracy of the algorithm. Firstly, use actual priori probability instead of constant probability of spam. Secondly, expand the selective range and rule of tokens. Finally, add URLs and images into detection content. Keywords-spam; statistical-based Bayesian Classification; content detection
D.j. States - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Classification of protein structure
IEEE Expert, 1992Co-Authors: Lawrence Hunter, D.j. StatesAbstract:The application of an Autoclass III machine-learning algorithm for heuristic Bayesian Classification to independently confirm and provide new information about structural classes of proteins is discussed. Bayesian Classification and Autoclass III systems are reviewed. The decisions concerning and data representations and where to begin searching the space of potential Classifications are discussed. Results of the application, in terms of Classification patterns, relationship to traditional classes, higher-order structure, and class composition, are presented. >
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Applying Bayesian Classification to protein structure
[1991] Proceedings. The Seventh IEEE Conference on Artificial Intelligence Application, 1Co-Authors: Lawrence Hunter, D.j. StatesAbstract:A report is given on the advantages of Bayesian Classification over traditional methods and the challenges in applying the Autoclass III program, a heuristic Bayesian classifier, in the domain of biotechnology and protein structure Classification. The machine learning technique of heuristic Bayesian Classification specifically addresses the question of how many classes a dataset should be divided into, as well as what the Classifications should be. The method is based on a minimal message length description of the dataset. The cost (in bits) of specifying a Classification is added to the cost of accounting for each exemplar in terms of its distance from the class definition and the total cost is minimized. In addition to providing a well founded estimate of the number of classes necessary to optimally characterize a dataset, this method also generates test Classifications where within-class variances differ significantly. >
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Bayesian Classification of protein structural elements
Proceedings of the Twenty-Fourth Annual Hawaii International Conference on System Sciences, 1Co-Authors: Lawrence Hunter, D.j. StatesAbstract:Heuristic Bayesian Classification is applied to the problem of defining and enumerating structural motifs present in protein macromolecular systems. The Classification system used, Autoclass III, estimates the most probable number of classes and describes class membership in terms of both mean attribute value and expected variance. A database of 9556 protein structure segments, each five amino acids in length, was compiled from high resolution, high quality X-ray crystal structures in the Brookhaven Protein Databank. Coordinates for these segments were transformed to a translationally and rotationally invariant local reference frame, then classified. >
Jing Wang - One of the best experts on this subject based on the ideXlab platform.
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Attribute weighted Naive Bayesian Classification algorithm
2010 5th International Conference on Computer Science & Education, 2010Co-Authors: Chunying Zhang, Jing WangAbstract:Naive Bayes algorithm is a simple and efficient Classification algorithm, but its conditional independence assumption is not always true in real life which is affected to some extent. Weighted Naive Bayesian classifier relax the conditional independence assumption to increase accuracy. Based on Identifiably matrix of Rough Set, a new weighted naive Bayes method based on attribute frequency is proposed. Different condition attributes are weighted differently; the Naive Bayesian Classification algorithm performance is improved effectively. Experiments have proved that the calculation of this algorithm is easier and more effective.
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FSKD - Multi-relational Bayesian Classification Algorithm with Rough Set
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010Co-Authors: Chunying Zhang, Jing WangAbstract:A Multi-relational Bayesian Classification Algorithm with Rough Set is proposed in this paper. The concept of relational graph used to dynamic choice associative table associated with the target table, and a tuple ID propagation approach is used to solve directly the association rule mining problem with multiple database relations, and the concept of Core in Rough Set is introduced, simplify the associative table. Compared with the traditional algorithm,it improves the accuracy rate. Experimental results show that its running rate is much higher than that of Bayesian Classification Algorithm and Graph_NB Algorithm.
