Rank of Normalizers Through TOPSIS with the Help of Supervised Classifiers

  • Authors

    • Ranjit Panigrahi
    • Samarjeet Borah
    https://doi.org/10.14419/ijet.v7i3.24.22798
  • Normalization, Support Vector Machine, SVM, TOPSIS, KNN, Neural Network, Naive Bayes, k-Nearest Neighbors, Linear Regression.
  • Abstract

    Classification is a tedious task for gathering and categorizing collected knowledge from the noisy high-dimensional dataset. The classifier suffers a lot when the dimension of the dataset is high and the underlying dataset is in different size and units. To make the classification cost effective, the dataset must be subject to pre-processing. Normalization, as a pre-processor transforms the data into a unit less mode across all the dimensions of the dataset. Practically there are many normalization techniques which are best suitable for different implementation scenarios. Though, it is believed that normalization improves classifiers performance but it is a tedious task to ascertain an optimum normalizer for specific scenarios. In this paper, seven widely used normalization techniques are evaluated through five popular supervised learning classifiers using intrusion detection dataset. Rank to these normalization techniques are allocated using a popular ranking algorithm called as Techniques for Order Preference by Similarity to the Ideal Solution (TOPSIS), thus revealing the best optimum normalizer for intrusion detection environment.

     

     


     
  • References

    1. [1] Jordan MI, Mitchell TM, Machine learning: Trends, perspectives, and prospects, Science Journal, 2015; 349(6245):255-260

      [2] Lobur M, Stekh Y, Artsibasov V, Challenges in knowledge discovery and data mining in datasets, Perspective Technologies and Methods in MEMS Design, Polyana, 2011; 232-233

      [3] Eftekhary M, Gholami P, Safari S, et al., Ranking Normalization Methods for Improving the Accuracy of SVM Algorithm by DEA Method, Modern Applied Science,2012; 6(10):26-36

      [4] Sianaki OA, Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid. (PhD), Curtin University, 2015, Retrieved from http://espace. library. curtin. edu.au/R?func=dbin-jump-full&local_base=gen01-era02&object_id=240088 (240088)

      [5] Jailani R, Hashim H, Nasir Taib M, Normalization techniques for psoriasis skin lesion analysis, Asian Conference on Sensors and the International Conference on new Techniques in Pharmaceutical and Biomedical Research, 2006; 151-153

      [6] Ahmed B, Abdullah Md., Noman Al, Land cover classification for satellite images based on normalization technique and Artificial Neural Network, International Conference on Computer and Information Engineering (ICCIE), Rajshahi, 2015; 138-141

      [7] Kabir W, Ahmad MO, Swamy MNS, A novel normalization technique for multimodal biometric systems, International Midwest Symposium on Circuits and Systems (MWSCAS), 2015; 1-4

      [8] Kasmin F, Abdullah A, Prabuwono AS, The effect of normalization techniques and their ensembles towards Otsu method, International Conference on Intelligent Systems Design and Applications (ISDA), 2012; 931–936

      [9] Patel R, Shrawankar U, Thakare VM, Normalization techniques for hiding speakers identity, International Conference on Data Science & Engineering (ICDSE), 2012; 75-80

      [10] Aguilar JF, Garcia JO, Rodriguez JG, Target dependent score normalization techniques and their application to signature verification, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2005; 35(3): 418–425

      [11] Lahby M, Cherkaoui L, Adib A, Performance analysis of normalization techniques for network selection access in heterogeneous wireless networks, International Conference on Intelligent Systems: Theories and Applications (SITA-14), Rabat, 2014; 1-5

      [12] Baghla S, Bansal S, Effect of normalization techniques in VIKOR method for network selection in heterogeneous networks, IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, 2014; 1–6

      [13] Gensler A, Sick B, Vogt S, A review of deterministic error scores and normalization techniques for power forecasting algorithms, IEEE Symposium Series on Computational Intelligence (SSCI), 2016; 1–9

      [14] Sever H, Tolun MR, Comparison of Normalization Techniques for Metasearch. In: Yakhno T. (eds) Advances in Information Systems, ADVIS, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2002;2457

      [15] Cortes C, Vapnik V, Support-vector network, Machine Learning, 1995; 20:1–25.

      [16] Burges CJC, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, 1998; 2(2): 1-43.

      [17] Pengwenlong Gu, Khatoun R, Begriche Y, et al. Support Vector Machine (SVM) Based Sybil Attack Detection in Vehicular Networks, IEEE Wireless Communications and Networking Conference (WCNC), pp. 1-6, March 2017

      [18] Poole D, Mackworth A, Linear Regression and Classification, Artificial Intelligence: Foundations of Computational Agents, Cambridge University Press, 2010

      [19] Pedregosa F, Varoquaux G, Gramfort A, et al., Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 2011;12-2825-2830.

      [20] d.umn.edu [Internet]. Duluth(MN): University of Minnesota; [cited 2017 July 20]. Available from: www.d.umn.edu/ ~deoka001 / downloads/ K_Nearest_ Neighbor _ Algorithm.pdf

      [21] Zhu X, Goldberg A, Introduction to Semi-Supervised Learning, Morgan and Claypool Publishers (CA, USA), 2009

      [22] Guleria P, Sood M, Educational Data Classification and Clustering Using K-Means and K-Nearest Neighbours, International Journal of Control Theory and Applications, 2016; 10(40):181-189.

      [23] Muthulakshmi S, Porkodi R, A Survey on Classification Algorithms in Data Mining of Bioinformatics, International Journal for Scientific Research & Development, 2016 ; 4(3): 612-617.

      [24] Hwang CL, Yoon K, Multiple Attributes Decision Making Methods and Applications. Springer, Berlin Heidelberg, 1981.

      [25] Lashkari AH, Gil GD, Mamun M ,Ghorbani A, Characterization of Tor Traffic using Time based Features, 3rd International Conference on Information Systems Security and Privacy, 2017; 1:253-262

      [26] Pradeep Kumar Mallick, Debahuti Mishra, Srikanta Patnaik and Kailash Shaw, “A Hybrid Approach for Simultaneous Gene Clustering and Gene Selection for Pattern Classificationâ€, Indian Journal of Science and Technology, Volume : 9(21), DOI: 10.17485/ijst/2016/v9i21/94175, June 2016.

      [27] Pradeep Kumar Mallick, Debahuti Mishra, Srikanta Patnaik and Kailash Shaw, “A Semi-Supervised Rough Set and Random Forest Approach for Pattern Classification of Gene Expression Dataâ€, Int. J. of Reasoning-based Intelligent Systems (IJRIS), Vol.8, No.3/4, pp.155 – 167, 2016.

      [28] Pradeep Kumar Mallick, Debahuti Mishra, Srikanta Patnaik and Kailash Shaw, “A Novel Supervised Gene Clustering Approach By Mining Interdependent Gene Patterns†International Journal of Pharma and Bio Sciences, Volume: 7(4): (B), pp: 20 - 32 ,2016.

  • Downloads

  • How to Cite

    Panigrahi, R., & Borah, S. (2018). Rank of Normalizers Through TOPSIS with the Help of Supervised Classifiers. International Journal of Engineering & Technology, 7(3.24), 483-490. https://doi.org/10.14419/ijet.v7i3.24.22798

    Received date: 2018-12-02

    Accepted date: 2018-12-02