An intelligent recursive feature reduction methods for efficient classification of medical blogs

  • Authors

    • Neeba E A
    • Koteeswaran S
    2018-02-05
    https://doi.org/10.14419/ijet.v7i1.7.10654
  • Blog Classification, Bagging, Logitboost, Adaboost, BFO and PSO

  • Abstract

    Nowadays people express themselvesin the form of weblogs attracting and benefiting users in their perspective. Blogs are maintained by individual users with specific software reporting activities that removing all possible barriers and developing learning blogs. Blogs together forms a linked community as blogosphere. Procuring information from these blogs has become very tough due to the intense growth of blogs. The proposed system classifies and compiles the blog ensuring appropriate chemo taxis analysis and reporting. These data preparation and analysis helps the healthcare organizations to solve many health related problems and identify new improvements. The movement of organism to chemical stimulus is recognized in chemo taxis. Chemo taxis is subverted in cancer phases. Weblogs not only manage and organize these data but also reduces the time required for data gathering, acquisition, analysis and reporting.

  • References

    1. [1]. Jin .W, Joty.S, Carenini. G and R,“Detecting Informative Blog Comments using Tree Structured Conditional Random Fieldsâ€.

      [2]. Ikeda.D, Takamura. H and Okumura. M, “Semi-Supervised Learning for Blog Classification, in AAA, pp. 1156-1161, July, 2008.

      [3]. Sun. X, Li. S, Xu. W, Chen. G and Guo. J, “Machine learning based blog classification personal vs. official facetâ€, Computer Research and Development,3rd International Conference, vol. 1, pp. 31-34, IEEE, March, 2013.

      [4]. Ayyasamy. R. K, Tahayna. B, Alhashmi. S. M., “ concept based modeling approach for blog classification using fuzzy similarityâ€,Fuzzy Systems and Knowledge Discovery, Eighth International Conference, vol. 2, pp. 1007-1011, IEEE, July 2011.

      [5]. Shi, C., Li, J., Chen, J., & Chen, X. (2013, August). Chinese SNS blog classification using semantic similarity. In Computational Aspects of Social Networks (CASoN), 2013 Fifth International Conference on (pp. 1-6). IEEE.

      [6]. Bai. R, Wang. X, and Liao. J, “Folksonomy for the blogosphere: Blog identification and classificationâ€, Computer Science and Information Engineering, 2009 WRI World Congress, Vol. 3, pp. 631-635.

      [7]. Gao. X, Liu. L, and Hou. S, “A method of micro-blog information classification based on mixed characteristicsâ€, Ninth International Conference on Natural Computation, pp. 853-857, IEEE, July 2013.

      [8]. Duan, X., He, T., & Song, L. (2010, August). Research on sentiment classification of Blog based on PMI-IR. In Natural Language Processing and Knowledge Engineering (NLP-KE), 2010 International Conference on (pp. 1-6). IEEE.

      [9]. Sun. X, Li. Cand Ren. F, “Modeling blog user for Chinese micro-blog sentiment classification with fuzzy SVM, IEEE 3rd International Conference on Cloud Computing and Intelligence Systems, pp. 130-134, November, 2017.

      [10]. Dalal, M. K., & Zaveri, M. A. (2012, January). Automatic text classification of sports blog data. In Computing, Communications and Applications Conference (ComComAp), 2012 (pp. 219-222). IEEE.

      [11]. Chen, Y., Guo, W., & Zhao, X. (2010, July). A Semantic Based Information Retrieval Model for Blog. In Electronic Commerce and Security (ISECS), 2010 Third International Symposium on (pp. 257-260). IEEE.

      [12]. Machová, K., Barcak, F., & Bednár, P. (2006). A bagging method using decision trees in the role of base classifiers. Acta Polytechnica Hungarica,3(2), 121-132.

      [13]. Lee, W. L., Lommatzsch, A., & Scheel, C. (2007, October). Feed Distillation Using AdaBoost and Topic Maps. In TREC (Vol. 7, p. 21).

      [14]. Li, P. (2012). Robust logitboost and adaptive base class (abc) logitboost.arXiv preprint arXiv:1203.3491.

      [15]. Ghorpade-Aher. J and Bagdiya. R, “A Review on Clustering Web data using PSOâ€, International Journal of Computer Applications, 108, 2014.

      [16]. Sharma, V., Pattnaik, S. S., & Garg, T. (2012). A Review of Bacterial Foraging Optimization and Its Applications. In National Conference on Future Aspects of Artificial intelligence in Industrial Automation, NCFAAIIA (pp. 9-12).

  • Downloads

  • How to Cite

    E A, N., & S, K. (2018). An intelligent recursive feature reduction methods for efficient classification of medical blogs. International Journal of Engineering & Technology, 7(1.7), 205-209. https://doi.org/10.14419/ijet.v7i1.7.10654

    Received date: 2018-03-26

    Accepted date: 2018-03-26

    Published date: 2018-02-05