Analysis of supervised classification techniques

  • Authors

    • P. Lakhmi Prasanna
    • D. Rajeswara Rao
    • Y. Meghana
    • K. Maithri
    • T. Dhinesh
    2017-12-21
    https://doi.org/10.14419/ijet.v7i1.1.9486
  • KNN, Naïve Bayes, Support Vector, Decision Tree, ANN.
  • Abstract

    As the number of digital documents and data are being increased rapidly, it is important to classify them in to respective categories. This process of classifying the data is called classification. There are three ways in to which the data can be classified un supervised, supervised and semi supervised methods. Automatic Text Classification is done by supervised learning techniques. This paper discusses about various classification techniques, their advantages and limitations. Finally, it concludes with the best classification technique. In this paper the best classification technique that was proposed is Artificial Neural Network (ANN). The reason for proposing ANN as the best algorithm is given and its application in various important fields was given.

  • References

    1. [1] “A Review of Machine Learning Algorithms for Text Document Classification†by Aurangzeb Khan, Baharum Baharudin, Lam Hong Lee, Khairullah khan.

      [2] “Survey of Text classification Algorithms†by Charu C. Aggarwal

      [3] “Comparison of Text Classification Algorithms†by M. Trivedi, S. Sharma, N. Soni, S. Nair

      [4] “Review on Classification Based on Artificial Neural Networks†by Saravanan K and S. Sasithra

      [5] “Functional Analysis of Artificial Neural Network for Dataset Classification†by Rojal ina Priyadarshini, Nillamadhab Dash, Tripti Swarnkar, Rachita Misra.

      [6] “An overview on Text Classification Techniques†by Dinesh Tharwani

      [7] “Classification Using ANN: A Review†by Rajni Bala, Dr.Dharmender Kumar

      [8] C. C. Aggarwal, S. C. Gates, P. S. Yu. On Using Partial Supervision for Text Categorization, IEEE Transactions on Knowledge and Data Engineering

      [9] C. Apte, F. Damerau, S. Weiss. Automated Learning of Decision Rules for Text Categorization

      [10] Rojalina Priyadarshini; “Functional Analysis of Artificial Neural Network for Dataset Classificationâ€.

      [11] Guoqiang Peter Zhang “Neural Network for Classification- A Survey 2000†IEEE Transactions on systems, man and cybermetics- part c: applications and reviews, Vol 30

      [12] “Neural Network based classification of car seat fabrics†International Journal of Mathematical Models and Methods in Applied Sciences by R. Furferi, L. Governi.

      [13] E. Hosseini Aria, J. Amini, M.R. Saradjian, “Back Propagation Neural Network for Classification of IRS-1D Satellite Images†Vol.1, Issue.2, 2003.

      [14] Helena Grip, Fredrik Öhberg, Urban Wiklund, Ylva Sterner, J. Stefan Karlsson, and Björn Gerdle, “Classification of Neck Movement Patterns Related to Whiplash-Associated Disorders Using Neural Networksâ€, IEEE transactions on information technology in biomedicine, Vol.7, Issue.4,2003. https://doi.org/10.1109/TITB.2003.821322.

      [15] Guoqiang Peter Zhang, “Classification of Breast Cancer Data with Harmony Search and Back Propagation Based Artificial Neural Networkâ€, IEEE 22nd Signal Processing and Communications Applications Conference, 2014.

      [16] Abid Ali, Olaf Magnor and Matthias Schultalbers, “Misfire Detection Using a Neural Network Based Pattern Recognitionâ€, “International Conference on Artificial Intelligence and Computational Intelligenceâ€, Vol.2, Issue.3, 2009.

  • Downloads

  • How to Cite

    Lakhmi Prasanna, P., Rajeswara Rao, D., Meghana, Y., Maithri, K., & Dhinesh, T. (2017). Analysis of supervised classification techniques. International Journal of Engineering & Technology, 7(1.1), 283-285. https://doi.org/10.14419/ijet.v7i1.1.9486

    Received date: 2018-02-11

    Accepted date: 2018-02-11

    Published date: 2017-12-21