The Experts below are selected from a list of 15594 Experts worldwide ranked by ideXlab platform
Kai Qian - One of the best experts on this subject based on the ideXlab platform.
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the impact of data preprocessing on the performance of a naive Bayes Classifier
Computer Software and Applications Conference, 2016Co-Authors: Priyanga Chandrasekar, Kai QianAbstract: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.
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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, 2016Co-Authors: Tianda Yang, Kai Qian, Dan Chia-tien LoAbstract: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.
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Spam filtering using Association Rules and Naïve Bayes Classifier
2015 IEEE International Conference on Progress in Informatics and Computing (PIC), 2015Co-Authors: Tianda Yang, Kai Qian, Dan Chia-tien Lo, Kamal Al Nasr, Ying QianAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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privacy preserving naive Bayes Classifier for vertically partitioned data
SIAM International Conference on Data Mining, 2004Co-Authors: Jaideep Vaidya, Chris CliftonAbstract: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.
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SDM - Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data.
2004Co-Authors: Jaideep Vaidya, Chris CliftonAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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ICB - A novel PCA-based Bayes Classifier and face analysis
Advances in Biometrics, 2006Co-Authors: Franck Davoine, Jingyu YangAbstract: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.
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A novel PCA-based Bayes Classifier and face analysis
Lecture Notes in Computer Science, 2006Co-Authors: Franck Davoine, Jingyu YangAbstract: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.
Jonghoon Chun - One of the best experts on this subject based on the ideXlab platform.
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modified naive Bayes Classifier for e catalog classification
International Conference on Data Engineering, 2006Co-Authors: Jonghoon ChunAbstract:As the wide use of online business transactions, the volume of product information that needs to be managed in a system has become drastically large, and the classification task of such data has become highly complex. The heterogeneity among competing standard classification schemes makes the problem only harder. However, the classification task is an indispensable part for successful e-commerce applications. In this paper, we present an automated approach for e-catalog classification. We extend the Naive Bayes Classifier to make use of the structural characteristics of e-catalogs. We show how we can improve the accuracy of classification when appropriate characteristics of e-catalogs are utilized. Effectiveness of the proposed methods is validated through experiments.
Jaideep Vaidya - One of the best experts on this subject based on the ideXlab platform.
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privacy preserving naive Bayes Classifier for vertically partitioned data
SIAM International Conference on Data Mining, 2004Co-Authors: Jaideep Vaidya, Chris CliftonAbstract: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.
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SDM - Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data.
2004Co-Authors: Jaideep Vaidya, Chris CliftonAbstract: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.