As digital technologies have advanced more rapidly,the number of paper documents recently converted into a digital format has exponentially increased.To respond to the urgent need to categorize the growing number of d...As digital technologies have advanced more rapidly,the number of paper documents recently converted into a digital format has exponentially increased.To respond to the urgent need to categorize the growing number of digitized documents,the classification of digitized documents in real time has been identified as the primary goal of our study.A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human involvement.Artificial intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits,which were not conceivable ten years ago.Deep learning aids in comprehending input patterns so that object classes may be predicted.The segmentation process divides the input image into separate segments for a more thorough image study.This study proposes a deep learning-enabled framework for automated document classification,which can be implemented in higher education.To further this goal,a dataset was developed that includes seven categories:Diplomas,Personal documents,Journal of Accounting of higher education diplomas,Service letters,Orders,Production orders,and Student orders.Subsequently,a deep learning model based on Conv2D layers is proposed for the document classification process.In the final part of this research,the proposed model is evaluated and compared with other machine-learning techniques.The results demonstrate that the proposed deep learning model shows high results in document categorization overtaking the other machine learning models by reaching 94.84%,94.79%,94.62%,94.43%,94.07%in accuracy,precision,recall,F-score,and AUC-ROC,respectively.The achieved results prove that the proposed deep model is acceptable to use in practice as an assistant to an office worker.展开更多
A rough set based corner classification neural network, the Rough-CC4, is presented to solve document classification problems such as document representation of different document sizes, document feature selection and...A rough set based corner classification neural network, the Rough-CC4, is presented to solve document classification problems such as document representation of different document sizes, document feature selection and document feature encoding. In the Rough-CC4, the documents are described by the equivalent classes of the approximate words. By this method, the dimensions representing the documents can be reduced, which can solve the precision problems caused by the different document sizes and also blur the differences caused by the approximate words. In the Rough-CC4, a binary encoding method is introduced, through which the importance of documents relative to each equivalent class is encoded. By this encoding method, the precision of the Rough-CC4 is improved greatly and the space complexity of the Rough-CC4 is reduced. The Rough-CC4 can be used in automatic classification of documents.展开更多
The Internet of Medical Things(IoMT)will come to be of great importance in the mediation of medical disputes,as it is emerging as the core of intelligent medical treatment.First,IoMT can track the entire medical treat...The Internet of Medical Things(IoMT)will come to be of great importance in the mediation of medical disputes,as it is emerging as the core of intelligent medical treatment.First,IoMT can track the entire medical treatment process in order to provide detailed trace data in medical dispute resolution.Second,IoMT can infiltrate the ongoing treatment and provide timely intelligent decision support to medical staff.This information includes recommendation of similar historical cases,guidance for medical treatment,alerting of hired dispute profiteers etc.The multi-label classification of medical dispute documents(MDDs)plays an important role as a front-end process for intelligent decision support,especially in the recommendation of similar historical cases.However,MDDs usually appear as long texts containing a large amount of redundant information,and there is a serious distribution imbalance in the dataset,which directly leads to weaker classification performance.Accordingly,in this paper,a multi-label classification method based on key sentence extraction is proposed for MDDs.The method is divided into two parts.First,the attention-based hierarchical bi-directional long short-term memory(BiLSTM)model is used to extract key sentences from documents;second,random comprehensive sampling Bagging(RCS-Bagging),which is an ensemble multi-label classification model,is employed to classify MDDs based on key sentence sets.The use of this approach greatly improves the classification performance.Experiments show that the performance of the two models proposed in this paper is remarkably better than that of the baseline methods.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
Many algorithms have been implemented for the problem of document categorization. The majority work in this area was achieved for English text, while a very few approaches have been introduced for the Arabic text. The...Many algorithms have been implemented for the problem of document categorization. The majority work in this area was achieved for English text, while a very few approaches have been introduced for the Arabic text. The nature of Arabic text is different from that of the English text and the preprocessing of the Arabic text is more challenging. This is due to Arabic language is a highly inflectional and derivational language that makes document mining a hard and complex task. In this paper, we present an Automatic Arabic documents classification system based on kNN algorithm. Also, we develop an approach to solve keywords extraction and reduction problems by using Document Frequency (DF) threshold method. The results indicate that the ability of the kNN to deal with Arabic text outperforms the other existing systems. The proposed system reached 0.95 micro-recall scores with 850 Arabic texts in 6 different categories.展开更多
This paper presents a new improved term frequency/inverse document frequency (TF-IDF) approach which uses confidence, support and characteristic words to enhance the recall and precision of text classification. Synony...This paper presents a new improved term frequency/inverse document frequency (TF-IDF) approach which uses confidence, support and characteristic words to enhance the recall and precision of text classification. Synonyms defined by a lexicon are processed in the improved TF-IDF approach. We detailedly discuss and analyze the relationship among confidence, recall and precision. The experiments based on science and technology gave promising results that the new TF-IDF approach improves the precision and recall of text classification compared with the conventional TF-IDF approach.展开更多
Many text classifications depend on statistical term measures to implement document representation. Such document representations ignore the lexical semantic contents of terms and the distilled mutual information, lea...Many text classifications depend on statistical term measures to implement document representation. Such document representations ignore the lexical semantic contents of terms and the distilled mutual information, leading to text classification errors.This work proposed a document representation method, Word Net-based lexical semantic VSM, to solve the problem. Using Word Net,this method constructed a data structure of semantic-element information to characterize lexical semantic contents, and adjusted EM modeling to disambiguate word stems. Then, in the lexical-semantic space of corpus, lexical-semantic eigenvector of document representation was built by calculating the weight of each synset, and applied to a widely-recognized algorithm NWKNN. On text corpus Reuter-21578 and its adjusted version of lexical replacement, the experimental results show that the lexical-semantic eigenvector performs F1 measure and scales of dimension better than term-statistic eigenvector based on TF-IDF. Formation of document representation eigenvectors ensures the method a wide prospect of classification applications in text corpus analysis.展开更多
Since webpage classification is different from traditional text classification with its irregular words and phrases,massive and unlabeled features,which makes it harder for us to obtain effective feature.To cope with ...Since webpage classification is different from traditional text classification with its irregular words and phrases,massive and unlabeled features,which makes it harder for us to obtain effective feature.To cope with this problem,we propose two scenarios to extract meaningful strings based on document clustering and term clustering with multi-strategies to optimize a Vector Space Model(VSM) in order to improve webpage classification.The results show that document clustering work better than term clustering in coping with document content.However,a better overall performance is obtained by spectral clustering with document clustering.Moreover,owing to image existing in a same webpage with document content,the proposed method is also applied to extract image meaningful terms,and experiment results also show its effectiveness in improving webpage classification.展开更多
Hyper-connectivity in Industry 4.0 has resulted in not only a rapid increase in the amount of information,but also the expansion of areas and assets to be protected.In terms of information security,it has led to an en...Hyper-connectivity in Industry 4.0 has resulted in not only a rapid increase in the amount of information,but also the expansion of areas and assets to be protected.In terms of information security,it has led to an enormous economic cost due to the various and numerous security solutions used in protecting the increased assets.Also,it has caused difficulties in managing those issues due to reasons such as mutual interference,countless security events and logs’data,etc.Within this security environment,an organization should identify and classify assets based on the value of data and their security perspective,and then apply appropriate protection measures according to the assets’security classification for effective security management.But there are still difficulties stemming from the need to manage numerous security solutions in order to protect the classified assets.In this paper,we propose an information classification management service based on blockchain,which presents and uses a model of the value of data and the security perspective.It records transactions of classifying assets and managing assets by each class in a distributed ledger of blockchain.The proposed service reduces assets to be protected and security solutions to be applied,and provides security measures at the platform level rather than individual security solutions,by using blockchain.In the rapidly changing security environment of Industry 4.0,this proposed service enables economic security,provides a new integrated security platform,and demonstrates service value.展开更多
A novel association-based algorithm EmailinClass is proposed for incremental Email classification. In view of the fact that the basic semantic unit in an Email is actually a sentence, and the words within the same sen...A novel association-based algorithm EmailinClass is proposed for incremental Email classification. In view of the fact that the basic semantic unit in an Email is actually a sentence, and the words within the same sentence are typically more semantically related than the words that just appear in the same Email, EmailInClass views a sentence rather than an Email as a transaction. Extensive experiments conducted on benchmark corpora Enron reveal that the effectiveness of EmallInClass is superior to the non-incremental alternatives such as NalveBayes and SAT-MOD. In addition, the classification rules generated by EroaillnClass are human readable and revisable,展开更多
Sentiment analysis is now more and more important in modern natural language processing,and the sentiment classification is the one of the most popular applications.The crucial part of sentiment classification is feat...Sentiment analysis is now more and more important in modern natural language processing,and the sentiment classification is the one of the most popular applications.The crucial part of sentiment classification is feature extraction.In this paper,two methods for feature extraction,feature selection and feature embedding,are compared.Then Word2Vec is used as an embedding method.In this experiment,Chinese document is used as the corpus,and tree methods are used to get the features of a document:average word vectors,Doc2Vec and weighted average word vectors.After that,these samples are fed to three machine learning algorithms to do the classification,and support vector machine(SVM) has the best result.Finally,the parameters of random forest are analyzed.展开更多
One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification ...One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification problem for second-order tensor data. Traditional vector-based one-class classification methods such as one-class support vector machine (OCSVM) and least squares one-class support vector machine (LSOCSVM) have limitations when tensor is used as input data, so we propose a new tensor one-class classification method, LSOCSTM, which directly uses tensor as input data. On one hand, using tensor as input data not only enables to classify tensor data, but also for vector data, classifying it after high dimensionalizing it into tensor still improves the classification accuracy and overcomes the over-fitting problem. On the other hand, different from one-class support tensor machine (OCSTM), we use squared loss instead of the original loss function so that we solve a series of linear equations instead of quadratic programming problems. Therefore, we use the distance to the hyperplane as a metric for classification, and the proposed method is more accurate and faster compared to existing methods. The experimental results show the high efficiency of the proposed method compared with several state-of-the-art methods.展开更多
文摘As digital technologies have advanced more rapidly,the number of paper documents recently converted into a digital format has exponentially increased.To respond to the urgent need to categorize the growing number of digitized documents,the classification of digitized documents in real time has been identified as the primary goal of our study.A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human involvement.Artificial intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits,which were not conceivable ten years ago.Deep learning aids in comprehending input patterns so that object classes may be predicted.The segmentation process divides the input image into separate segments for a more thorough image study.This study proposes a deep learning-enabled framework for automated document classification,which can be implemented in higher education.To further this goal,a dataset was developed that includes seven categories:Diplomas,Personal documents,Journal of Accounting of higher education diplomas,Service letters,Orders,Production orders,and Student orders.Subsequently,a deep learning model based on Conv2D layers is proposed for the document classification process.In the final part of this research,the proposed model is evaluated and compared with other machine-learning techniques.The results demonstrate that the proposed deep learning model shows high results in document categorization overtaking the other machine learning models by reaching 94.84%,94.79%,94.62%,94.43%,94.07%in accuracy,precision,recall,F-score,and AUC-ROC,respectively.The achieved results prove that the proposed deep model is acceptable to use in practice as an assistant to an office worker.
基金The National Natural Science Foundation of China(No.60503020,60373066,60403016,60425206),the Natural Science Foundation of Jiangsu Higher Education Institutions ( No.04KJB520096),the Doctoral Foundation of Nanjing University of Posts and Telecommunication (No.0302).
文摘A rough set based corner classification neural network, the Rough-CC4, is presented to solve document classification problems such as document representation of different document sizes, document feature selection and document feature encoding. In the Rough-CC4, the documents are described by the equivalent classes of the approximate words. By this method, the dimensions representing the documents can be reduced, which can solve the precision problems caused by the different document sizes and also blur the differences caused by the approximate words. In the Rough-CC4, a binary encoding method is introduced, through which the importance of documents relative to each equivalent class is encoded. By this encoding method, the precision of the Rough-CC4 is improved greatly and the space complexity of the Rough-CC4 is reduced. The Rough-CC4 can be used in automatic classification of documents.
基金supported by the National Key R&D Program of China(2018YFC0830200,Zhang,B,www.most.gov.cn)the Fundamental Research Funds for the Central Universities(2242018S30021 and 2242017S30023,Zhou S,www.seu.edu.cn)the Open Research Fund from Key Laboratory of Computer Network and Information Integration In Southeast University,Ministry of Education,China(3209012001C3,Zhang B,www.seu.edu.cn).
文摘The Internet of Medical Things(IoMT)will come to be of great importance in the mediation of medical disputes,as it is emerging as the core of intelligent medical treatment.First,IoMT can track the entire medical treatment process in order to provide detailed trace data in medical dispute resolution.Second,IoMT can infiltrate the ongoing treatment and provide timely intelligent decision support to medical staff.This information includes recommendation of similar historical cases,guidance for medical treatment,alerting of hired dispute profiteers etc.The multi-label classification of medical dispute documents(MDDs)plays an important role as a front-end process for intelligent decision support,especially in the recommendation of similar historical cases.However,MDDs usually appear as long texts containing a large amount of redundant information,and there is a serious distribution imbalance in the dataset,which directly leads to weaker classification performance.Accordingly,in this paper,a multi-label classification method based on key sentence extraction is proposed for MDDs.The method is divided into two parts.First,the attention-based hierarchical bi-directional long short-term memory(BiLSTM)model is used to extract key sentences from documents;second,random comprehensive sampling Bagging(RCS-Bagging),which is an ensemble multi-label classification model,is employed to classify MDDs based on key sentence sets.The use of this approach greatly improves the classification performance.Experiments show that the performance of the two models proposed in this paper is remarkably better than that of the baseline methods.
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.
基金Supported by National High Technology Research and Development Program of China （863 Program） （2008AA01Z144） National Natural Science Foundation of China （60803093 60975055）
文摘Many algorithms have been implemented for the problem of document categorization. The majority work in this area was achieved for English text, while a very few approaches have been introduced for the Arabic text. The nature of Arabic text is different from that of the English text and the preprocessing of the Arabic text is more challenging. This is due to Arabic language is a highly inflectional and derivational language that makes document mining a hard and complex task. In this paper, we present an Automatic Arabic documents classification system based on kNN algorithm. Also, we develop an approach to solve keywords extraction and reduction problems by using Document Frequency (DF) threshold method. The results indicate that the ability of the kNN to deal with Arabic text outperforms the other existing systems. The proposed system reached 0.95 micro-recall scores with 850 Arabic texts in 6 different categories.
基金Project (No. 60082003) supported by the National Natural Science Foundation of China
文摘This paper presents a new improved term frequency/inverse document frequency (TF-IDF) approach which uses confidence, support and characteristic words to enhance the recall and precision of text classification. Synonyms defined by a lexicon are processed in the improved TF-IDF approach. We detailedly discuss and analyze the relationship among confidence, recall and precision. The experiments based on science and technology gave promising results that the new TF-IDF approach improves the precision and recall of text classification compared with the conventional TF-IDF approach.
基金Project(2012AA011205)supported by National High-Tech Research and Development Program(863 Program)of ChinaProjects(61272150,61379109,M1321007,61301136,61103034)supported by the National Natural Science Foundation of China+1 种基金Project(20120162110077)supported by Research Fund for the Doctoral Program of Higher Education of ChinaProject(11JJ1012)supported by Excellent Youth Foundation of Hunan Scientific Committee,China
文摘Many text classifications depend on statistical term measures to implement document representation. Such document representations ignore the lexical semantic contents of terms and the distilled mutual information, leading to text classification errors.This work proposed a document representation method, Word Net-based lexical semantic VSM, to solve the problem. Using Word Net,this method constructed a data structure of semantic-element information to characterize lexical semantic contents, and adjusted EM modeling to disambiguate word stems. Then, in the lexical-semantic space of corpus, lexical-semantic eigenvector of document representation was built by calculating the weight of each synset, and applied to a widely-recognized algorithm NWKNN. On text corpus Reuter-21578 and its adjusted version of lexical replacement, the experimental results show that the lexical-semantic eigenvector performs F1 measure and scales of dimension better than term-statistic eigenvector based on TF-IDF. Formation of document representation eigenvectors ensures the method a wide prospect of classification applications in text corpus analysis.
基金supported by the National Natural Science Foundation of China under Grants No.61100205,No.60873001the HiTech Research and Development Program of China under Grant No.2011AA010705the Fundamental Research Funds for the Central Universities under Grant No.2009RC0212
文摘Since webpage classification is different from traditional text classification with its irregular words and phrases,massive and unlabeled features,which makes it harder for us to obtain effective feature.To cope with this problem,we propose two scenarios to extract meaningful strings based on document clustering and term clustering with multi-strategies to optimize a Vector Space Model(VSM) in order to improve webpage classification.The results show that document clustering work better than term clustering in coping with document content.However,a better overall performance is obtained by spectral clustering with document clustering.Moreover,owing to image existing in a same webpage with document content,the proposed method is also applied to extract image meaningful terms,and experiment results also show its effectiveness in improving webpage classification.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2020-2018-0-01799)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation).
文摘Hyper-connectivity in Industry 4.0 has resulted in not only a rapid increase in the amount of information,but also the expansion of areas and assets to be protected.In terms of information security,it has led to an enormous economic cost due to the various and numerous security solutions used in protecting the increased assets.Also,it has caused difficulties in managing those issues due to reasons such as mutual interference,countless security events and logs’data,etc.Within this security environment,an organization should identify and classify assets based on the value of data and their security perspective,and then apply appropriate protection measures according to the assets’security classification for effective security management.But there are still difficulties stemming from the need to manage numerous security solutions in order to protect the classified assets.In this paper,we propose an information classification management service based on blockchain,which presents and uses a model of the value of data and the security perspective.It records transactions of classifying assets and managing assets by each class in a distributed ledger of blockchain.The proposed service reduces assets to be protected and security solutions to be applied,and provides security measures at the platform level rather than individual security solutions,by using blockchain.In the rapidly changing security environment of Industry 4.0,this proposed service enables economic security,provides a new integrated security platform,and demonstrates service value.
基金The National Natural Science Foundationof China (No.60303030) and the Natural Science Foundationof Chongqing (No.8721)
文摘A novel association-based algorithm EmailinClass is proposed for incremental Email classification. In view of the fact that the basic semantic unit in an Email is actually a sentence, and the words within the same sentence are typically more semantically related than the words that just appear in the same Email, EmailInClass views a sentence rather than an Email as a transaction. Extensive experiments conducted on benchmark corpora Enron reveal that the effectiveness of EmallInClass is superior to the non-incremental alternatives such as NalveBayes and SAT-MOD. In addition, the classification rules generated by EroaillnClass are human readable and revisable,
基金National Natural Science Foundation of China(No.71331008)
文摘Sentiment analysis is now more and more important in modern natural language processing,and the sentiment classification is the one of the most popular applications.The crucial part of sentiment classification is feature extraction.In this paper,two methods for feature extraction,feature selection and feature embedding,are compared.Then Word2Vec is used as an embedding method.In this experiment,Chinese document is used as the corpus,and tree methods are used to get the features of a document:average word vectors,Doc2Vec and weighted average word vectors.After that,these samples are fed to three machine learning algorithms to do the classification,and support vector machine(SVM) has the best result.Finally,the parameters of random forest are analyzed.
文摘One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification problem for second-order tensor data. Traditional vector-based one-class classification methods such as one-class support vector machine (OCSVM) and least squares one-class support vector machine (LSOCSVM) have limitations when tensor is used as input data, so we propose a new tensor one-class classification method, LSOCSTM, which directly uses tensor as input data. On one hand, using tensor as input data not only enables to classify tensor data, but also for vector data, classifying it after high dimensionalizing it into tensor still improves the classification accuracy and overcomes the over-fitting problem. On the other hand, different from one-class support tensor machine (OCSTM), we use squared loss instead of the original loss function so that we solve a series of linear equations instead of quadratic programming problems. Therefore, we use the distance to the hyperplane as a metric for classification, and the proposed method is more accurate and faster compared to existing methods. The experimental results show the high efficiency of the proposed method compared with several state-of-the-art methods.
