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Bayes Classifier

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Kai Qian – 1st expert on this subject based on the ideXlab platform

  • the impact of data preprocessing on the performance of a naive Bayes Classifier
    Computer Software and Applications Conference, 2016
    Co-Authors: Priyanga Chandrasekar, Kai Qian

    Abstract:

    In the research of text mining, document classification is a growing field. Even though we have many existing classifying approaches, Naive Bayes Classifier is simple and effective at classification. Data preprocessing is the important step in the data mining process. It prepares the raw data for the further process. The aim of this paper is to identify the impact of preprocessing the dataset on the performance of a Naive Bayes Classifier. The Naive Bayes Classifier is suggested as the most effective method to identify the spam emails. The Impact of preprocessing phase on the performance of the Naive Bayes Classifier is analyzed by comparing the output of both the preprocessed dataset result and non-preprocessed dataset result. The test results show that combining Naive Bayes classification with the proper data preprocessing can improve the prediction accuracy.

  • Improve the Prediction Accuracy of Naïve Bayes Classifier with Association Rule Mining
    2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity) IEEE International Conference on High Performance and Smart Com, 2016
    Co-Authors: Tianda Yang, Kai Qian, Dan Chia-tien Lo

    Abstract:

    Nowadays, big data contains infinite business opportunities. Companies begin to analyze their data to predict their potential customers and business decisions using Naive Bayes Classifier, Association Rule Mining, Decision Tree and other famous algorithms. An accurate classification result may help companies leading in its industry. Companies seek to find feasible business intelligences to obtain reliable prediction results. In this paper we propose an association rule mining to improve Naive Bayes Classifier. Naive Bayes Classifier is one of the famous algorithm in big data classification but based on an independent assumptions between features. Association rule mining is popular and useful for discovering relations between inputs in big data analysis. We use bank marketing data set to illustrate in this work. In general, this work is helpful to all the business data set.

  • Spam filtering using Association Rules and Naïve Bayes Classifier
    2015 IEEE International Conference on Progress in Informatics and Computing (PIC), 2015
    Co-Authors: Tianda Yang, Kai Qian, Dan Chia-tien Lo, Kamal Al Nasr, Ying Qian

    Abstract:

    E-mail service is one of the most popular Internet communication services. Thousands of companies, organizations and individuals use e-mail every day and get benefit from it. However, an amount of spam emails always hang around us and bring down our productivity. We urgently need a spam filtering to clean up our network environment. A spam filtering using Association Rule and Naive Bayes Classifier is recommended here. Instead of focusing on increasing spam precision rate, we try to preserve all non-spam emails as the first priority. In the real world applications and services, that’s what we should do. In this paper, we also provide the comparison between using both Association Rule and Naive Bayes Classifier algorithms and just using Naive Bayes Classifier.

Chris Clifton – 2nd expert on this subject based on the ideXlab platform

  • privacy preserving naive Bayes Classifier for vertically partitioned data
    SIAM International Conference on Data Mining, 2004
    Co-Authors: Jaideep Vaidya, Chris Clifton

    Abstract:

    The problem of secure distributed classification is an important one. In many situations, data is split between multiple organizations. These organizations may want to utilize all of the data to create more accurate predictive models while revealing neither their training data / databases nor the instances to be classified. The Naive Bayes Classifier is a simple but efficient baseline Classifier. In this paper, we present a privacy preserving Naive Bayes Classifier for horizontally partitioned data.

  • SDM – Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data.
    , 2004
    Co-Authors: Jaideep Vaidya, Chris Clifton

    Abstract:

    The problem of secure distributed classification is an important one. In many situations, data is split between multiple organizations. These organizations may want to utilize all of the data to create more accurate predictive models while revealing neither their training data / databases nor the instances to be classified. The Naive Bayes Classifier is a simple but efficient baseline Classifier. In this paper, we present a privacy preserving Naive Bayes Classifier for horizontally partitioned data.

Jingyu Yang – 3rd expert on this subject based on the ideXlab platform

  • ICB – A novel PCA-based Bayes Classifier and face analysis
    Advances in Biometrics, 2006
    Co-Authors: Franck Davoine, Jingyu Yang

    Abstract:

    The classical Bayes Classifier plays an important role in the field of pattern recognition. Usually, it is not easy to use a Bayes Classifier for pattern recognition problems in high dimensional spaces. This paper proposes a novel PCA-based Bayes Classifier for pattern recognition problems in high dimensional spaces. Experiments for face analysis have been performed on CMU facial expression image database. It is shown that the PCA-based Bayes Classifier can perform much better than the minimum distance Classifier. And, with the PCA-based Bayes Classifier, we can obtain a better understanding of data.

  • A novel PCA-based Bayes Classifier and face analysis
    Lecture Notes in Computer Science, 2006
    Co-Authors: Franck Davoine, Jingyu Yang

    Abstract:

    The classical Bayes Classifier plays an important role in the field of pattern recognition. Usually, it is not easy to use a Bayes Classifier for pattern recognition problems in high dimensional spaces. This paper proposes a novel PCA-based Bayes Classifier for pattern recognition problems in high dimensional spaces. Experiments for face analysis have been performed on CMU facial expression image database. It is shown that the PCA-based Bayes Classifier can perform much better than the minimum distance Classifier. And, with the PCA-based Bayes Classifier, we can obtain a better understanding of data.