The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parall...The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parallel naive Bayes algorithm(PNBA)for Chinese text classification based on Spark,a parallel memory computing platform for big data.This algorithm has implemented parallel operation throughout the entire training and prediction process of naive Bayes classifier mainly by adopting the programming model of resilient distributed datasets(RDD).For comparison,a PNBA based on Hadoop is also implemented.The test results show that in the same computing environment and for the same text sets,the Spark PNBA is obviously superior to the Hadoop PNBA in terms of key indicators such as speedup ratio and scalability.Therefore,Spark-based parallel algorithms can better meet the requirement of large-scale Chinese text data mining.展开更多
Many companies like credit card, insurance, bank, retail industry require direct marketing. Data mining can help those institutes to set marketing goal. Data mining techniques have good prospects in their target audie...Many companies like credit card, insurance, bank, retail industry require direct marketing. Data mining can help those institutes to set marketing goal. Data mining techniques have good prospects in their target audiences and improve the likelihood of response. In this work we have investigated two data mining techniques: the Naive Bayes and the C4.5 decision tree algorithms. The goal of this work is to predict whether a client will subscribe a term deposit. We also made comparative study of performance of those two algorithms. Publicly available UCI data is used to train and test the performance of the algorithms. Besides, we extract actionable knowledge from decision tree that focuses to take interesting and important decision in business area.展开更多
In recent times among the multitude of attacks present in network system, DDoS attacks have emerged to be the attacks with the most devastating effects. The main objective of this paper is to propose a system that eff...In recent times among the multitude of attacks present in network system, DDoS attacks have emerged to be the attacks with the most devastating effects. The main objective of this paper is to propose a system that effectively detects DDoS attacks appearing in any networked system using the clustering technique of data mining followed by classification. This method uses a Heuristics Clustering Algorithm (HCA) to cluster the available data and Na?ve Bayes (NB) classification to classify the data and detect the attacks created in the system based on some network attributes of the data packet. The clustering algorithm is based in unsupervised learning technique and is sometimes unable to detect some of the attack instances and few normal instances, therefore classification techniques are also used along with clustering to overcome this classification problem and to enhance the accuracy. Na?ve Bayes classifiers are based on very strong independence assumptions with fairly simple construction to derive the conditional probability for each relationship. A series of experiment is performed using “The CAIDA UCSD DDoS Attack 2007 Dataset” and “DARPA 2000 Dataset” and the efficiency of the proposed system has been tested based on the following performance parameters: Accuracy, Detection Rate and False Positive Rate and the result obtained from the proposed system has been found that it has enhanced accuracy and detection rate with low false positive rate.展开更多
Spam is a universal problem with which everyone is familiar. A number of approaches are used for Spam filtering. The most common filtering technique is content-based filtering which uses the actual text of message to ...Spam is a universal problem with which everyone is familiar. A number of approaches are used for Spam filtering. The most common filtering technique is content-based filtering which uses the actual text of message to determine whether it is Spam or not. The content is very dynamic and it is very challenging to represent all information in a mathematical model of classification. For instance, in content-based Spam filtering, the characteristics used by the filter to identify Spam message are constantly changing over time. Na?ve Bayes method represents the changing nature of message using probability theory and support vector machine (SVM) represents those using different features. These two methods of classification are efficient in different domains and the case of Nepali SMS or Text classification has not yet been in consideration;these two methods do not consider the issue and it is interesting to find out the performance of both the methods in the problem of Nepali Text classification. In this paper, the Na?ve Bayes and SVM-based classification techniques are implemented to classify the Nepali SMS as Spam and non-Spam. An empirical analysis for various text cases has been done to evaluate accuracy measure of the classification methodologies used in this study. And, it is found to be 87.15% accurate in SVM and 92.74% accurate in the case of Na?ve Bayes.展开更多
Purpose:With more and more digital collections of various information resources becoming available,also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization syst...Purpose:With more and more digital collections of various information resources becoming available,also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization systems.While the ultimate purpose is to understand the value of automatically produced Dewey Decimal Classification(DDC)classes for Swedish digital collections,the paper aims to evaluate the performance of six machine learning algorithms as well as a string-matching algorithm based on characteristics of DDC.Design/methodology/approach:State-of-the-art machine learning algorithms require at least 1,000 training examples per class.The complete data set at the time of research involved 143,838 records which had to be reduced to top three hierarchical levels of DDC in order to provide sufficient training data(totaling 802 classes in the training and testing sample,out of 14,413 classes at all levels).Findings:Evaluation shows that Support Vector Machine with linear kernel outperforms other machine learning algorithms as well as the string-matching algorithm on average;the string-matching algorithm outperforms machine learning for specific classes when characteristics of DDC are most suitable for the task.Word embeddings combined with different types of neural networks(simple linear network,standard neural network,1 D convolutional neural network,and recurrent neural network)produced worse results than Support Vector Machine,but reach close results,with the benefit of a smaller representation size.Impact of features in machine learning shows that using keywords or combining titles and keywords gives better results than using only titles as input.Stemming only marginally improves the results.Removed stop-words reduced accuracy in most cases,while removing less frequent words increased it marginally.The greatest impact is produced by the number of training examples:81.90%accuracy on the training set is achieved when at least 1,000 records per class are available in the training set,and 66.13%when too few records(often less than A Comparison of Approaches100 per class)on which to train are available—and these hold only for top 3 hierarchical levels(803 instead of 14,413 classes).Research limitations:Having to reduce the number of hierarchical levels to top three levels of DDC because of the lack of training data for all classes,skews the results so that they work in experimental conditions but barely for end users in operational retrieval systems.Practical implications:In conclusion,for operative information retrieval systems applying purely automatic DDC does not work,either using machine learning(because of the lack of training data for the large number of DDC classes)or using string-matching algorithm(because DDC characteristics perform well for automatic classification only in a small number of classes).Over time,more training examples may become available,and DDC may be enriched with synonyms in order to enhance accuracy of automatic classification which may also benefit information retrieval performance based on DDC.In order for quality information services to reach the objective of highest possible precision and recall,automatic classification should never be implemented on its own;instead,machine-aided indexing that combines the efficiency of automatic suggestions with quality of human decisions at the final stage should be the way for the future.Originality/value:The study explored machine learning on a large classification system of over 14,000 classes which is used in operational information retrieval systems.Due to lack of sufficient training data across the entire set of classes,an approach complementing machine learning,that of string matching,was applied.This combination should be explored further since it provides the potential for real-life applications with large target classification systems.展开更多
An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the...An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the base for the Naive Bayes classifier to approve the effectiveness of the domain ontology for document classification. The 1752 documents divided into 10 categories are used to assess the effectiveness of the ontology, where 1252 and 500 documents are the training and testing documents, respectively. The Fl-measure is as the assessment criteria and the following three results are obtained. The average recall of Naive Bayes classifier is 0.94. Therefore, in recall, the performance of Naive Bayes classifier is excellent based on the automatically constructed ontology. The average precision of Naive Bayes classifier is 0.81. Therefore, in precision, the performance of Naive Bayes classifier is gored based on the automatically constructed ontology. The average Fl-measure for 10 categories by Naive Bayes classifier is 0.86. Therefore, the performance of Naive Bayes classifier is effective based on the automatically constructed ontology in the point of F 1-measure. Thus, the domain ontology automatically constructed could indeed be acted as the document categories to reach the effectiveness for document classification.展开更多
This paper deals with a real-life application of epilepsy classification, where three phases of absence seizure, namely pre-seizure, seizure and seizure-free, are classified using real clinical data. Artificial neural...This paper deals with a real-life application of epilepsy classification, where three phases of absence seizure, namely pre-seizure, seizure and seizure-free, are classified using real clinical data. Artificial neural network （ANN） and support vector machines （SVMs） combined with su- pervised learning algorithms, and k-means clustering （k-MC） combined with unsupervised techniques are employed to classify the three seizure phases. Different techniques to combine binary SVMs, namely One Vs One （OvO）, One Vs All （OVA） and Binary Decision Tree （BDT）, are employed for multiclass classification. Comparisons are performed with two traditional classification methods, namely, k-Nearest Neighbour （k- NN） and Naive Bayes classifier. It is concluded that SVM-based classifiers outperform the traditional ones in terms of recognition accuracy and robustness property when the original clinical data is distorted with noise. Furthermore, SVM-based classifier with OvO provides the highest recognition accuracy, whereas ANN-based classifier overtakes by demonstrating maximum accuracy in the presence of noise.展开更多
基金Project(KC18071)supported by the Application Foundation Research Program of Xuzhou,ChinaProjects(2017YFC0804401,2017YFC0804409)supported by the National Key R&D Program of China
文摘The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parallel naive Bayes algorithm(PNBA)for Chinese text classification based on Spark,a parallel memory computing platform for big data.This algorithm has implemented parallel operation throughout the entire training and prediction process of naive Bayes classifier mainly by adopting the programming model of resilient distributed datasets(RDD).For comparison,a PNBA based on Hadoop is also implemented.The test results show that in the same computing environment and for the same text sets,the Spark PNBA is obviously superior to the Hadoop PNBA in terms of key indicators such as speedup ratio and scalability.Therefore,Spark-based parallel algorithms can better meet the requirement of large-scale Chinese text data mining.
文摘Many companies like credit card, insurance, bank, retail industry require direct marketing. Data mining can help those institutes to set marketing goal. Data mining techniques have good prospects in their target audiences and improve the likelihood of response. In this work we have investigated two data mining techniques: the Naive Bayes and the C4.5 decision tree algorithms. The goal of this work is to predict whether a client will subscribe a term deposit. We also made comparative study of performance of those two algorithms. Publicly available UCI data is used to train and test the performance of the algorithms. Besides, we extract actionable knowledge from decision tree that focuses to take interesting and important decision in business area.
基金The authors would like to extend their gratitude to Department of Graduate StudiesNepal College of Information Technology for its constant support and motivationWe would also like to thank the Journal of Information Security for its feedbacks and reviews
文摘In recent times among the multitude of attacks present in network system, DDoS attacks have emerged to be the attacks with the most devastating effects. The main objective of this paper is to propose a system that effectively detects DDoS attacks appearing in any networked system using the clustering technique of data mining followed by classification. This method uses a Heuristics Clustering Algorithm (HCA) to cluster the available data and Na?ve Bayes (NB) classification to classify the data and detect the attacks created in the system based on some network attributes of the data packet. The clustering algorithm is based in unsupervised learning technique and is sometimes unable to detect some of the attack instances and few normal instances, therefore classification techniques are also used along with clustering to overcome this classification problem and to enhance the accuracy. Na?ve Bayes classifiers are based on very strong independence assumptions with fairly simple construction to derive the conditional probability for each relationship. A series of experiment is performed using “The CAIDA UCSD DDoS Attack 2007 Dataset” and “DARPA 2000 Dataset” and the efficiency of the proposed system has been tested based on the following performance parameters: Accuracy, Detection Rate and False Positive Rate and the result obtained from the proposed system has been found that it has enhanced accuracy and detection rate with low false positive rate.
文摘Spam is a universal problem with which everyone is familiar. A number of approaches are used for Spam filtering. The most common filtering technique is content-based filtering which uses the actual text of message to determine whether it is Spam or not. The content is very dynamic and it is very challenging to represent all information in a mathematical model of classification. For instance, in content-based Spam filtering, the characteristics used by the filter to identify Spam message are constantly changing over time. Na?ve Bayes method represents the changing nature of message using probability theory and support vector machine (SVM) represents those using different features. These two methods of classification are efficient in different domains and the case of Nepali SMS or Text classification has not yet been in consideration;these two methods do not consider the issue and it is interesting to find out the performance of both the methods in the problem of Nepali Text classification. In this paper, the Na?ve Bayes and SVM-based classification techniques are implemented to classify the Nepali SMS as Spam and non-Spam. An empirical analysis for various text cases has been done to evaluate accuracy measure of the classification methodologies used in this study. And, it is found to be 87.15% accurate in SVM and 92.74% accurate in the case of Na?ve Bayes.
文摘Purpose:With more and more digital collections of various information resources becoming available,also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization systems.While the ultimate purpose is to understand the value of automatically produced Dewey Decimal Classification(DDC)classes for Swedish digital collections,the paper aims to evaluate the performance of six machine learning algorithms as well as a string-matching algorithm based on characteristics of DDC.Design/methodology/approach:State-of-the-art machine learning algorithms require at least 1,000 training examples per class.The complete data set at the time of research involved 143,838 records which had to be reduced to top three hierarchical levels of DDC in order to provide sufficient training data(totaling 802 classes in the training and testing sample,out of 14,413 classes at all levels).Findings:Evaluation shows that Support Vector Machine with linear kernel outperforms other machine learning algorithms as well as the string-matching algorithm on average;the string-matching algorithm outperforms machine learning for specific classes when characteristics of DDC are most suitable for the task.Word embeddings combined with different types of neural networks(simple linear network,standard neural network,1 D convolutional neural network,and recurrent neural network)produced worse results than Support Vector Machine,but reach close results,with the benefit of a smaller representation size.Impact of features in machine learning shows that using keywords or combining titles and keywords gives better results than using only titles as input.Stemming only marginally improves the results.Removed stop-words reduced accuracy in most cases,while removing less frequent words increased it marginally.The greatest impact is produced by the number of training examples:81.90%accuracy on the training set is achieved when at least 1,000 records per class are available in the training set,and 66.13%when too few records(often less than A Comparison of Approaches100 per class)on which to train are available—and these hold only for top 3 hierarchical levels(803 instead of 14,413 classes).Research limitations:Having to reduce the number of hierarchical levels to top three levels of DDC because of the lack of training data for all classes,skews the results so that they work in experimental conditions but barely for end users in operational retrieval systems.Practical implications:In conclusion,for operative information retrieval systems applying purely automatic DDC does not work,either using machine learning(because of the lack of training data for the large number of DDC classes)or using string-matching algorithm(because DDC characteristics perform well for automatic classification only in a small number of classes).Over time,more training examples may become available,and DDC may be enriched with synonyms in order to enhance accuracy of automatic classification which may also benefit information retrieval performance based on DDC.In order for quality information services to reach the objective of highest possible precision and recall,automatic classification should never be implemented on its own;instead,machine-aided indexing that combines the efficiency of automatic suggestions with quality of human decisions at the final stage should be the way for the future.Originality/value:The study explored machine learning on a large classification system of over 14,000 classes which is used in operational information retrieval systems.Due to lack of sufficient training data across the entire set of classes,an approach complementing machine learning,that of string matching,was applied.This combination should be explored further since it provides the potential for real-life applications with large target classification systems.
文摘An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the base for the Naive Bayes classifier to approve the effectiveness of the domain ontology for document classification. The 1752 documents divided into 10 categories are used to assess the effectiveness of the ontology, where 1252 and 500 documents are the training and testing documents, respectively. The Fl-measure is as the assessment criteria and the following three results are obtained. The average recall of Naive Bayes classifier is 0.94. Therefore, in recall, the performance of Naive Bayes classifier is excellent based on the automatically constructed ontology. The average precision of Naive Bayes classifier is 0.81. Therefore, in precision, the performance of Naive Bayes classifier is gored based on the automatically constructed ontology. The average Fl-measure for 10 categories by Naive Bayes classifier is 0.86. Therefore, the performance of Naive Bayes classifier is effective based on the automatically constructed ontology in the point of F 1-measure. Thus, the domain ontology automatically constructed could indeed be acted as the document categories to reach the effectiveness for document classification.
文摘This paper deals with a real-life application of epilepsy classification, where three phases of absence seizure, namely pre-seizure, seizure and seizure-free, are classified using real clinical data. Artificial neural network （ANN） and support vector machines （SVMs） combined with su- pervised learning algorithms, and k-means clustering （k-MC） combined with unsupervised techniques are employed to classify the three seizure phases. Different techniques to combine binary SVMs, namely One Vs One （OvO）, One Vs All （OVA） and Binary Decision Tree （BDT）, are employed for multiclass classification. Comparisons are performed with two traditional classification methods, namely, k-Nearest Neighbour （k- NN） and Naive Bayes classifier. It is concluded that SVM-based classifiers outperform the traditional ones in terms of recognition accuracy and robustness property when the original clinical data is distorted with noise. Furthermore, SVM-based classifier with OvO provides the highest recognition accuracy, whereas ANN-based classifier overtakes by demonstrating maximum accuracy in the presence of noise.
