Text classification has been highlighted as the key process to organize online texts for better communication in the Digital Media Age. Text classification establishes classification rules based on text features, so the accuracy of feature selection is the basis of text classification. Facing fast-increasing Chinese electronic documents in the digital environment, scholars have accumulated quite a few algorithms for the feature selection for the automatic classification of Chinese texts in recent years. However, discussion about how to adapt existing feature selection algorithms for various types of Chinese texts is still inadequate. To address this, this study proposes three improved feature selection algorithms and tests their performance on different types of Chinese texts. These include an enhanced CHI square with mutual information (MI) algorithm, which simultaneously introduces word frequency and term adjustment (CHMI); a term frequency–CHI square (TF–CHI) algorithm, which enhances weight calculation; and a term frequency–inverse document frequency (TF–IDF) algorithm enhanced with the extreme gradient boosting (XGBoost) algorithm, which improves the algorithm’s ability of word filtering (TF–XGBoost). This study randomly chooses 3000 texts from six different categories of the Sogou news corpus to obtain the confusion matrix and evaluate the performance of the new algorithms with precision and the F1-score. Experimental comparisons are conducted on support vector machine (SVM) and naive Bayes (NB) classifiers. The experimental results demonstrate that the feature selection algorithms proposed in this paper improve performance across various news corpora, although the best feature selection schemes for each type of corpus are different. Further studies of the application of the improved feature selection methods in other languages and the improvement in classifiers are suggested.

Adapting Feature Selection Algorithms for the Classification of Chinese Texts

A search system for searching for electronic documents, and providing a search result in response to a search query is provided. The search system includes a processor, a user interface module adapted to receive a search query from a user, the search query having at least one search query term, and a query processing module that analyzes the search query term to identify candidate synonym words. The query processing module also determines which of the candidate synonym words are equivalent terms to the search query term, and in a same sense as the search query term. In addition, the search system includes a search engine that executes a search based on the search query term and the equivalent terms.

System and method for searching for a query

The rise of online social media is providing a wealth of social network data. Data mining techniques provide researchers and practitioners the tools needed to analyze large, complex, and frequently changing social media data. This chapter introduces the basics of data mining, reviews social media, discusses how to mine social media data, and highlights some illustrative examples with an emphasis on social networking sites and blogs.

Data Mining in Social Media

The problem of discriminating between two finite point sets in n-dimensional feature space by a separating plane that utilizes as few of the features as possible is formulated as a mathematical program with a parametric objective function and linear constraints. The step function that appears in the objective function can be approximated by a sigmoid or by a concave exponential on the nonnegative real line, or it can be treated exactly by considering the equivalent linear program with equilibrium constraints. Computational tests of these three approaches on publicly available real-world databases have been carried out and compared with an adaptation of the optimal brain damage method for reducing neural network complexity. One feature selection algorithm via concave minimization reduced cross-validation error on a cancer prognosis database by 35.4% while reducing problem features from 32 to 4.

Feature Selection via Mathematic Programming

Information Security and Assurance.- Fuzzy Based Threat Analysis in Total Hospital Information System.- An ID-Based Anonymous Signcryption Scheme for Multiple Receivers Secure in the Standard Model.- A Supervised Locality Preserving Projections Based Local Matching Algorithm for Face Recognition.- Information Systems Security Criticality and Assurance Evaluation.- Security Analysis of 'Two-Factor User Authentication in Wireless Sensor Networks'.- Directed Graph Pattern Synthesis in LSB Technique on Video Steganography.- Feature Level Fusion of Face and Palmprint Biometrics by Isomorphic Graph-Based Improved K-Medoids Partitioning.- Post-quantum Cryptography: Code-Based Signatures.- Security Analysis of the Proposed Practical Security Mechanisms for High Speed Data Transfer Protocol.- A Fuzzy-Based Dynamic Provision Approach for Virtualized Network Intrusion Detection Systems.- An Active Intrusion Detection System for LAN Specific Attacks.- Analysis on the Improved SVD-Based Watermarking Scheme.- Advanced Communication and Networking.- Applications of Adaptive Belief Propagation Decoding for Long Reed-Solomon Codes.- Dynamic Routing for Mitigating the Energy Hole Based on Heuristic Mobile Sink in Wireless Sensor Networks.- Grammar Encoding in DNA-Like Secret Sharing Infrastructure.- HATS: High Accuracy Timestamping System Based on NetFPGA.- A Roadside Unit Placement Scheme for Vehicular Telematics Networks.- Concurrent Covert Communication Channels.- Energy Efficiency of Collaborative Communication with Imperfect Frequency Synchronization in Wireless Sensor Networks.- High Performance MAC Architecture for 3GPP Modem.- Modified Structures of Viterbi Alogrithm for Forced-State Method in Concatenated Coding System of ISDB-T.- A New Cross-Layer Unstructured P2P File Sharing Protocol over Mobile Ad Hoc Network.- A Model for Interference on Links in Inter-working Multi-hop Wireless Networks.- An Optimum ICA Based Multiuser Data Separation for Short Message Service.- Advanced Computer Science and Information Technology.- Multiple Asynchronous Requests on a Client-Based Mashup Page.- Using an Integrated Ontology Database to Categorize Web Pages.- Topic Detection by Topic Model Induced Distance Using Biased Initiation.- Mining Significant Least Association Rules Using Fast SLP-Growth Algorithm.- Maximized Posteriori Attributes Selection from Facial Salient Landmarks for Face Recognition.- Agent Based Approach to Regression Testing.- A Numerical Study on B&B Algorithms for Solving Sum-Of-Ratios Problem.- Development of a Digital Textbook Standard Format Based on XML.- A Pattern-Based Representation Approach for Online Discourses.- A Fault Tolerant Architecture for Transportation Information Services of E-Government.- Design and Implementation of Binary Tree Based Proactive Routing Protocols for Large MANETS.- Extract Semantic Information from WordNet to Improve Text Classification Performance.- Managing Ubiquitous Scientific Knowledge on Semantic Web.- A Semantic Pattern Approach to Managing Scientific Publications.- A Bootstrap Software Reliability Assessment Method to Squeeze Out Remaining Faults.- Markov Chain Monte Carlo Random Testing.- An Integrated Approach to Detect Fault-Prone Modules Using Complexity and Text Feature Metrics.- Ubiquitous Computing and Multimedia Applications.- An Effective Video Steganography Method for Biometric Identification.- A Video Coding Technique Using Octagonal Motion Search and BTC-PF Method for Fast Reconstruction.- Rough Set Approach in Ultrasound Biomicroscopy Glaucoma Analysis.- Video Copy Detection: Sequence Matching Using Hypothesis Test.- An XML-Based Digital Textbook and Its Educational Effectiveness.- SIMACT: A 3D Open Source Smart Home Simulator for Activity Recognition.- Design of an Efficient Message Collecting Scheme for the Slot-Based Wireless Mesh Network.- A Novel Approach Based on Fault Tolerance and Recursive Segmentation to Query by Humming.- Chinese Prosody Generation Based on C-ToBI Representation for Text-To-Speech.- CAS4UA: A Context-Aware Service System Based on Workflow Model for Ubiquitous Agriculture.- A Power Control Scheme for an Energy-Efficient MAC Protocol.- Towards the Designing of a Robust Intrusion Detection System through an Optimized Advancement of Neural Networks.

Advances in Computer Science and Information Technology

Since a decade, text categorization has become an active field of research in the machine learning community. Most of the approaches are based on the term occurrence frequency. The performance of such surface-based methods can decrease when the texts are too complex, i.e., ambiguous. One alternative is to use the semantic-based approaches to process textual documents according to their meaning. In this paper, we propose a Concept-based Vector Space Model which reflects the more abstract version of the semantic information instead of the Vector Space Model for the text. This model adjusts the weight of the Vector Space by importing the hypernymy-hyponymy relation between synonymy sets and the Concept Chain in the WordNet. Experimental results on several data sets show that the proposed approach, conception built from Wordnet, can achieve significant improvements with respect to the baseline algorithm.

Extract semantic information from Wordnet to improve text classification performance

Automatic categorization of documents into pre-defined taxonomies is a crucial step in data mining and knowledge discovery. Standard machine learning techniques like support vector machines(SVM) and related large margin methods have been successfully applied for this task. Unfortunately, the high dimensionality of input feature vectors impacts on the classification speed. The kernel parameters setting for SVM in a training process impacts on the classification accuracy. Feature selection is another factor that impacts classification accuracy. The objective of this work is to reduce the dimension of feature vectors, optimizing the parameters to improve the SVM classification accuracy and speed. In order to improve classification speed we spent rough sets theory to reduce the feature vector space. We present a genetic algorithm approach for feature selection and parameters optimization to improve classification accuracy. Experimental results indicate our method is more effective than traditional SVM methods and other traditional methods.

Combination of rough sets and genetic algorithms for text classification

For centuries, classification has been used to provide context and direction in any aspect of human knowledge. Standard machine learning techniques like support vector machines and related large margin methods have been successfully applied for this task. Unfortunately, automatic classifiers often conduct misclassifications. Folksonomy, a new manual classification scheme based on tagging efforts of users with freely chosen keywords can effective resolve this problem. In folksonomy, a user attaches tags to an item for their own classification, and they reflect many one's viewpoints. Since tags are chosen from users' vocabulary and contain many one's viewpoints, classification results are easy to understand for ordinary users. Even though the scalability of folksonomy is much higher than the other manual classification schemes, the method cannot deal with tremendous number of items such as whole weblog articles on the Internet. For the purpose of solving this problem, we propose a new classification method FSVMC (folisonomy and support vector machine classifier). The FSVMC uses support vector machines as a Tag-agent which is a program to determine whether a particular tag should be attached to a weblog page and Folksonomy dedicates to categorize the weblog articles. In addition, we propose a method to create a candidate tag database which is a list of tags that may be attached to weblog pages. Experimental results indicate our method is more flexible and effective than traditional methods.

Chinese weblog pages classification based on folksonomy and support vector machines

As we know, current classification methods are mostly based on the VSM (Vector Space Model), which only accounts for term frequency in the documents, and ignores important semantic relationships between key terms. We proposed a system that uses an integrated ontologies and Natural Language Processing techniques to index texts. Traditional Words matrix is replaced by Concepts based matrix. For this purpose, we developed fully automated methods for mapping keywords to their corresponding ontology concepts. Support Vector Machine a successful machine learning technique is used for classification. Experimental results shows that our proposed method dose improve text classification performance significantly.

Using an integrated ontology database to categorize web pages

A Novel Mapping Mechanism Model Between Different Ontology

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