Choosing a spectacular feature selection technique for telecommunication industry using fuzzy TOPSIS MCDM

  • Authors

    • M. Durairaj Department of Computer Science and Engineering, Bharathidasan University, Tiruchirappalli, India
    • T. Poornappriya Department of Computer Science and Engineering, Bharathidasan University, Tiruchirappalli, India
    2019-04-21
    https://doi.org/10.14419/ijet.v7i4.14875
  • Feature Selection, Information Gain, Gain Ratio, Chi-Square Analysis, Relieff, Fisher, ANN, NB, SVM, Fuzzy TOPSIS.

  • Abstract

    Sampling and Feature Selection may be employed to depreciate processing time and hence diminishing discovery time of churn customer in the telecommunication industry. This article intends to evaluate the feature selection methods based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) ranking method. An Entropy-based TOPSIS method has used to recommend the one or more selections between choices, having multiple attributes. The five Ranking feature selection methods like Information Gain, Gain Ratio, Chi-Square, ReliefF and Fisher score are utilized to decrease the size of the telecommunication dataset. The classification technique like Artificial Neural Network, Naïve Bayes and Support Vector Machine are applied to determine the performance of the feature selection techniques. This Entropy-based TOPSIS method is utilized to examine and rank the feature selection methods to improve the churn customer prediction.

     

     

  • References

    1. [1] Coussement, Kristof, Stefan Lessmann, and Geert Verstraeten. "A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry." Decision Support Systems 95 (2017): 27-36. https://doi.org/10.1016/j.dss.2016.11.007.

      [2] Mahajan, Vishal, Richa Misra, and Renuka Mahajan. "Review on factors affecting customer churn in telecom sector." International Journal of Data Analysis Techniques and Strategies 9.2 (2017): 122-144. https://doi.org/10.1504/IJDATS.2017.085898.

      [3] Stripling, Eugen, et al. "Profit maximizing logistic model for customer churn prediction using genetic algorithms." Swarm and Evolutionary Computation 40 (2018): 116-130. https://doi.org/10.1016/j.swevo.2017.10.010.

      [4] Zhang, Rui, FeipingNie, and Xuelong Li. "Self-weighted supervised discriminative feature selection." IEEE transactions on neural networks and learning systems 29.8 (2018): 3913-3918. https://doi.org/10.1109/TNNLS.2017.2740341.

      [5] Sheikhpour, Razieh, et al. "A survey on semi-supervised feature selection methods." Pattern Recognition 64 (2017): 141-158. https://doi.org/10.1016/j.patcog.2016.11.003.

      [6] Kurth, Margaret H., et al. "Trends and applications of multi-criteria decision analysis: use in government agencies." Environment Systems and Decisions 37.2 (2017): 134-143. https://doi.org/10.1007/s10669-017-9644-7.

      [7] Gupta, Charu, and Amita Jain. "Fuzzy Multi-Criteria Decision Making and Fuzzy Information Gain Based Automotive Recommender System." North American Fuzzy Information Processing Society Annual Conference. Springer, Cham, 2017. https://doi.org/10.1007/978-3-319-67137-6_30.

      [8] Jadhav, Swati, Hongmei He, and Karl Jenkins. "Information gain directed genetic algorithm wrapper feature selection for credit rating." Applied Soft Computing 69 (2018): 541-553. https://doi.org/10.1016/j.asoc.2018.04.033.

      [9] Venkataraman, Sivakumar, and Rajalakshmi Selvaraj. "Optimal and Novel Hybrid Feature Selection Framework for Effective Data Classification." Advances in Systems, Control and Automation. Springer, Singapore, 2018. 499-514. https://doi.org/10.1007/978-981-10-4762-6_48.

      [10] Liu, Yang, Jian-Wu Bi, and Zhi-Ping Fan. "Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms." Expert Systems with Applications 80 (2017): 323-339. https://doi.org/10.1016/j.eswa.2017.03.042.

      [11] Thaseen, Ikram Sumaiya, and CherukuriAswani Kumar. "Intrusion detection model using fusion of chi-square feature selection and multi class SVM." Journal of King Saud University-Computer and Information Sciences 29.4 (2017): 462-472. https://doi.org/10.1016/j.jksuci.2015.12.004.

      [12] Urbanowicz, Ryan J., et al. "Relief-based feature selection: introduction and review." Journal of biomedical informatics (2018). https://doi.org/10.1016/j.jbi.2018.07.014.

      [13] Senawi, Azlyna, Hua-Liang Wei, and Stephen A. Billings. "A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking." Pattern Recognition67 (2017): 47-61. https://doi.org/10.1016/j.patcog.2017.01.026.

      [14] Onu, Pascal U., et al. "Evaluation of sustainable acid rain control options utilizing a fuzzy TOPSIS multi-criteria decision analysis model frame work." Journal of cleaner production 141 (2017): 612-625.

      [15] https://www.kaggle.com/becksddf/churn-in-telecoms-dataset. https://doi.org/10.1016/j.jclepro.2016.09.065.

      [16] Durairaj, M, and T.S Poornappriya. "A Review on Big Data Analytics Tools for Telecommunication Industry." International Journal of Control Theory and Applications. 9.27 (2016): 185-193. Print.

      [17] LakshmanaprabuS.K, Sachi Nandan Mohanty, K. Shankar, Arunkumar N, GustavoRamireze. “Optimal deep learning model for classification of lung cancer on CT imagesâ€, Future Generation Computer Systems. 2018. https://doi.org/10.1016/j.future.2018.10.009.

      [18] Lakshmanaprabu SK, K. Shankar, Deepak Gupta, Ashish Khanna, Joel J. P. C. Rodrigues, Plácido R. Pinheiro, Victor Hugo C. de Albuquerque. “Ranking Analysis for Online Customer Reviews of Products Using Opinion Mining with Clusteringâ€. Complexity, 2018: 1-9. https://doi.org/10.1155/2018/3569351.

      [19] K. Shankar, Lakshmanaprabu S.K, Deepak Gupta, Andino Maseleno, Victor Hugo C. de Albuquerque. Optimal Features Based Multi Kernel SVM Approach for Thyroid Disease Classification. The Journal of Supercomputing, 2018. https://doi.org/10.1007/s11227-018-2469-4.

      [20] Lakshmanaprabu SK, K. Shankar, Ashish Khanna, Deepak Gupta, Joel J. P. C. Rodrigues, Plácido R. Pinheiro, Victor Hugo C. de Albuquerque. Effective Features to Classify Big Data using Social Internet of Things. IEEE Access, 6 (2018): 24196-24204. https://doi.org/10.1109/ACCESS.2018.2830651.

      [21] Andino Maseleno, Alicia Y.C. Tang, Moamin A. Mahmoud, Marini Othman, Suntiaji Yudo Negoro, Soukaina Boukri, K. Shankar, Satria Abadi, Muhamad Muslihudin. “The Application of Decision Support System by Using Fuzzy Saw Method in Determining the Feasibility of Electrical Installations in Customer’s Houseâ€. International Journal of Pure and Applied Mathematics, 119. 16 (2018): 4277-4286.

      [22] Muhammad Muslihudin, Risma Wanti, Hardono, Nurfaizal, K. Shankar, Ilayaraja M, Andino Maseleno, Fauzi, Dwi Rohmadi Mustofa, Muhammad Masrur, Siti Mukodimah, “Prediction of Layer Chicken Disease using Fuzzy Analytical Hierarcy Processâ€, International Journal of Engineering & Technology, 7. 2.26 (2018): 90-94.

      [23] Eka Sugiyarti, Kamarul Azmi Jasmi, Bushrah Basiron, Miftachul Huda, K. Shankar, Andino Maseleno, “Decision Support System of Scholarship Grantee Selection using Data Miningâ€, International Journal of Pure and Applied Mathematics, 119.15 (2018): 2239-2249.

      [24] Tri Susilowati, Kamarul Azmi Jasmi, Bushrah Basiron, Miftachul Huda, K. Shankar, Andino Maseleno, Anis Julia, Sucipto, “Determination of Scholarship Recipients using Simple Additive Weighting Methodâ€, International Journal of Pure and Applied Mathematics, 119. 15 (2018): 2231-2238.

      [25] K. Shankar. “Prediction of Most Risk Factors in Hepatitis Disease using Apriori Algorithmâ€, Research Journal of Pharmaceutical, Biological and Chemical Sciences, 8. 5 (2017): 477-484.

      [26] Nur Aminudin, Eni Sundari, K. Shankar, P. Deepalakshmi, Fauzi, Rita Irviani, Andino Maseleno. “Weighted Product and Its Application to Measure Employee Performanceâ€, International Journal of Engineering & Technology, 7. 2.26 (2018): 102-108. https://doi.org/10.14419/ijet.v7i2.27.11574.

  • Downloads

  • How to Cite

    Durairaj, M., & Poornappriya, T. (2019). Choosing a spectacular feature selection technique for telecommunication industry using fuzzy TOPSIS MCDM. International Journal of Engineering & Technology, 7(4), 5856-5861. https://doi.org/10.14419/ijet.v7i4.14875

    Received date: 2018-06-30

    Accepted date: 2018-06-30

    Published date: 2019-04-21