Categorizing online news articles using penguin search optimization algorithm

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Online news is an emerging channel where the internet users can get news. Analyzing huge volume of online news articles is a challenging one, because online news articles are generated and updated time to time. Big data techniques are used to tackle this problem. In order to classify the news articles into different categories, an approach based on Evolving Fuzzy Systems(EFS) was used. It categories news articles based on the changes in the content of the corresponding articles. However, it has the problem in the selection of threshold value. Moreover Gaussian membership function is used in EFS that describes the closeness to the prototype. Sometimes it is hard to justify. So in this paper, a Penguins Search Optimization Algorithm(PeSOA) is introduced to optimize the pruning threshold value and a bell shaped fuzzy membership function is introduced to define the closeness to the prototype. The optimized pruning threshold is used in term filtering which prune the generated terms based on their frequencies of occurrence throughout the collection. Then the fuzzy rules are generated by EFS where bell shaped fuzzy membership function is used to define the closeness to the prototype. Based on the fuzzy rules the online news articles are categorized.


  • Keywords


    Bell Shaped Fuzzy Membership Function; Evolving Fuzzy System; Online News; Penguins Search Optimization Algorithm; Web News Mining

  • References


      [1] Karam R, Puri R, Bhunia S (2016), Energy-efficient adaptive hardware accelerator for text mining application kernels. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 24(12), pp. 3526-3537. https://doi.org/10.1109/TVLSI.2016.2555984.

      [2] Li Y, Algarni A, Albathan M, Shen Y, Bijaksana MA (2015), Relevance feature discovery for text mining. IEEE Transactions on Knowledge and Data Engineering, 27(6), pp. 1656-1669. https://doi.org/10.1109/TKDE.2014.2373357.

      [3] Dang S, & Ahmad PH (2014), Text mining: techniques and its application. International Journal of Engineering & Technology Innovations, 1(4), 22-25.

      [4] Nithya, D, Sivakumari, S (2017), State of the Art of Web News Mining. International Journal of Computer Engineering and Applications, 10(8), 122-129.

      [5] Nithya, D, Sivakumari, S (2017), A Study on Web Mining Tools. International Journal of Research in Electronics and Computer Engineering, 5(2), 135-137.

      [6] Wanjari YW, Mohod VD, Gaikwad DB, & Deshmukh SN (2014), Automatic news extraction system for Indian online newspapers. In third International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions) IEEE, pp. 1-6.

      [7] Iglesias JA, Tiemblo A, Ledezma A, Sanchis A (2016), Web news mining in an evolving framework. Information Fusion, 28, 2016; 90-98. https://doi.org/10.1016/j.inffus.2015.07.004.

      [8] Kaur S, Rashid EM (2016), Web news mining using back propagation neural network and clustering using K-Means algorithm in Big data. Indian Journal of Science and Technology, 9(41), pp. 1-8. https://doi.org/10.17485/ijst/2016/v9i41/95598.

      [9] Kaur S, Khiva NK (2016), online news classification using Deep Learning Technique. International Research Journal of Engineering and Technology (IRJET), 3(10), pp. 558-563.

      [10] Sharma N, Kaur P (2015), Categorize Online news using Various Classification Techniques. International Journal of Advanced Research in Computer Science & Technology (IJARCET), 4 (2), pp. 337-340.

      [11] Liparas D, HaCohen-Kerner Y, Moumtzidou A,Vrochidis S, Kompatsiaris I (2014), News articles classification using Random Forests and weighted multimodal features. In Information Retrieval Facility Conference Springer, Cham, pp. 63-75.

      [12] Longe HOD (2014), A Text Classifier Model for Categorizing Feed Contents Consumed by a Web Aggregator. International Journal of Advanced Computer Science and Applications (IJACSA), 5(9), pp. 95-100. https://doi.org/10.14569/IJACSA.2014.050915.

      [13] Gheraibia Y, Moussaoui A (2013), Penguins search optimization algorithm (PeSOA). In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems Springer, Berlin, Heidelberg, 2013; 222-231.

      [14] Huang W, Li Y (2012), Bell-Shaped Probabilistic Fuzzy Set for Uncertainties Modeling. Journal of Theoretical & Applied Information Technology, 46(2), pp. 875-882.


 

View

Download

Article ID: 15607
 
DOI: 10.14419/ijet.v7i4.15607




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.