An Identification Method of News Scientific Intelligence Based on TF-IDF

With the development of Internet, the amount of Information has been rapidly growing which is spread widely. In order to improve the value and accuracy of science information that is pushed in this paper, an intelligence dichotomous method for science information categorization to identify science information from massive Web news is presents. During the experiment, 85.3% recognition rate of the recognition non-tech news are realized and 82.9% accuracy rate, the results show that the method can effectively identify Web science information news and reduce the amount of independent news.

[1]  Yan Jia,et al.  A BP neural network text categorization method optimized by an improved genetic algorithm , 2013, 2013 Ninth International Conference on Natural Computation (ICNC).

[2]  Milos Manic,et al.  Clustering of web search results based on an Iterative Fuzzy C-means Algorithm and Bayesian Information Criterion , 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

[3]  Yi Guo,et al.  Content-Oriented Automatic Text Categorization with the Cognitive Situation Models , 2008, 2008 International Symposium on Computer Science and Computational Technology.

[4]  F. Ahmadizar,et al.  Two-stage text feature selection method using fuzzy entropy measure and an t colony optimization , 2012, 20th Iranian Conference on Electrical Engineering (ICEE2012).

[5]  Jian Yang,et al.  Study on Massive Text Classification Mining Grid System , 2010, 2010 2nd International Symposium on Information Engineering and Electronic Commerce.

[6]  Jin Shui,et al.  An Improved Mutual Information-Based Feature Selection Algorithm for Text Classification , 2013, 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[7]  Feng Qi,et al.  Improved information gain-based feature selection for text categorization , 2014, 2014 4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace & Electronic Systems (VITAE).

[8]  Wenbao Jiang,et al.  Study on the Subjective and Objective Text Classification and Pretreatment of Chinese Network Text , 2012, 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[9]  Yan Li,et al.  Research on the feature selection techniques used in text classification , 2012, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery.

[10]  P. Jaganathan,et al.  An improved K-means algorithm combined with Particle Swarm Optimization approach for efficient web document clustering , 2013, 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE).

[11]  Hongge Hu,et al.  A New Model for Chinese Short-text Classification Considering Feature Extension , 2010, 2010 International Conference on Artificial Intelligence and Computational Intelligence.

[12]  Yuefeng Li,et al.  Rough Set Based Approach to Text Classification , 2013, 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[13]  Yueming Lu,et al.  A blended feature selection method in text classification , 2013 .

[14]  Shuzlina Abdul Rahman,et al.  Exploring Feature Selection and Support Vector Machine in Text Categorization , 2013, 2013 IEEE 16th International Conference on Computational Science and Engineering.

[15]  M. Hemalatha,et al.  Automatic Text categorization and summarization using rule reduction , 2012, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012).

[16]  Li Guo,et al.  Latent Factor SVM for Text Categorization , 2014, 2014 IEEE International Conference on Data Mining Workshop.

[17]  Qiang Shen,et al.  A Rough Set-Based Approach to Text Classification , 1999, RSFDGrC.

[18]  Yang Bingru,et al.  Research on Short Text Classification Algorithm Based on Statistics and Rules , 2010, 2010 Third International Symposium on Electronic Commerce and Security.

[19]  Yi Lu Murphey,et al.  Automatic text categorization using a system of high-precision and high-recall models , 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[20]  Saleh Alshomrani,et al.  Hybrid ACO and TOFA feature selection approach for text classification , 2012, 2012 IEEE Congress on Evolutionary Computation.

[21]  Tian Yu,et al.  Chinese Web Text Classification System Model Based on Naive Bayes , 2010, 2010 International Conference on E-Product E-Service and E-Entertainment.

[22]  Tang Yan,et al.  Improved mutual information method for text feature selection , 2013, 2013 8th International Conference on Computer Science & Education.

[23]  Lu Han,et al.  Study on the construction of domain text classification model with the help of domain knowledge , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[24]  Mohammad R. Akbarzadeh-Totonchi,et al.  A hybrid type-2 fuzzy clustering technique for input data preprocessing of classification algorithms , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).