E-Commerce Product Classification Using Supervised Learning Models

  • Authors

    • Norsyela Muhammad Noor Mathivanan
    • Nor Azura Md. Ghani
    • Roziah Mohd Janor
    • . .
    2019-01-18
    https://doi.org/10.14419/ijet.v8i1.7.25979
  • Text Classification, E-commerce product, Supervised Learning Model

  • E-commerce has become a major player in today’s marketplace having a large database of products and number of retailers and consumers use these services. However, these products are placed into different categories according to the structure of different websites. An automatic classification model helps in classifying the products efficiently. This paper presents a comparative study on different algorithms from supervised learning model to classify real-world datasets related to e-commerce products. The results show that KNN is the best model with the highest accuracy to classify the data used in the study. Hence, KNN model is a good approach in classifying e-commerce products.

     

     

  • References

    1. [1] D. Kim, S. Lee, and J. Chun, “A semantic classification model for e-catalogs,†Proceedings. IEEE International Conference on e-Commerce Technology, 2004. CEC 2004., no. August, pp. 85–92, 2004.

      [2] C. Sun, N. Rampalli, F. Yang, and A. Doan, “Chimera: Large-scale Classification Using Machine Learning, Rules, and Crowdsourcing,†Proceedings of the VLDB Endowment, vol. 7, no. 13, pp. 1529–1540, 2014.

      [3] R. Florez-Lopez and J. M. Ramon-Jeronimo, “Marketing segmentation through machine learning models: An approach based on customer relationship management and customer profitability accounting,†Social Science Computer Review, 2009.

      [4] K. Balyan, K. S. McCarthy, and D. S. McNamara, “Combining machine learning and natural language processing to assess literacy text comprehension,†in Proceedings of the 10th International Conference on Educational Data Mining (EDM), 2017.

      [5] M. W. Libbrecht and W. S. Noble, “Machine learning applications in genetics and genomics,†Nature Reviews Genetics. 2015.

      [6] P. Sharma and M. Kaur, “Classification in Pattern Recognition: A Review,†International Journal of Advanced Research in Computer Science and Software Engineering, 2013.

      [7] R. Sathya and A. Abraham, “Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification,†International Journal of advanced Research in Artificial Intelligence, vol. 2, no. 2, pp. 34–38, 2013.

      [8] S. Ryszard Michalski, G. Carbonell Jamie, and M. Tom Mitchell, Machine learning: An Artificial Intelligence Approach. Morgan Kaufmann, 1985.

      [9] A. Dey, “Machine Learning Algorithms : A Review,†International Journal of Computer Science and Information Technologies, vol. 7, no. 3, pp. 1174–1179, 2016.

      [10] A. S. Patil and B. V. Pawar, “Automated Classification of Web Sites using Naive Bayesian Algorithm,†IMECS, vol. 1, 2012.

      [11] L. Devroye, “On the Inequality of Cover and Hart in Nearest Neighbor Discrimination,†IEEE Transactions on Pattern Analysis and Machine Intelligence, 1981.

      [12] N. Suguna and K. Thanushkodi, “An Improved k-Nearest Neighbor Classification Using Genetic Algorithm,†International Journal of Computer Science Issues, 2010.

      [13] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2012.

      [14] C. Cortes and V. Vapnik, “Support-Vector Networks,†Machine Learning, 1995.

      [15] C. D. A. Vanitha, D. Devaraj, and M. Venkatesulu, “Gene Expression Data Classification Using Support Vector Machine and Mutual Information-based Gene Selection,†Procedia Computer Science, 2015.

      [16] H. Yu and S. Kim, “15 - SVM Tutorial — Classification, Regression and Ranking,†Handbook of Natural Computing, 2012.

      [17] V. Y. Kulkarni and P. K. Sinha, “Effective Learning and Classification using Random Forest Algorithm,†International Journal of Engineering and Innovative Technolgy, vol. 3, no. 11, pp. 267–273, 2014.

      [18] G. Krishna, M. Nookala, N. Orsu, B. K. Pottumuthu, and S. B. Mudunuri, “Performance Analysis and Evaluation of Different Data Mining Algorithms used for Cancer Classification,†(IJARAI) International Journal of Advanced Research in Artificial Intelligence, 2013.

      [19] N. M. N. Mathivanan, N. A. M. Ghani, and R. M. Janor, “Improving Classification Accuracy Using Clustering Technique,†Bulletin of Electrical Engineering and Informatics, vol. 7, no. 3, pp. 465–470, 2018.

      [20] G. N. Ramadevi, K. U. Rani, and D. Lavanya, “Evaluation of Classifiers Performance using Resampling on Breast Cancer Data,†International Journal of Scientific & Engineering Research, vol. 6, no. 2, 2015.

      [21] X. Shao, H. Li, N. Wang, and Q. Zhang, “Comparison of different classification methods for analyzing electronic nose data to characterize sesame oils and blends,†Sensors (Switzerland), 2015.

      [22] D. R. Amancio et al., “A systematic comparison of supervised classifiers,†PLoS ONE, 2014.

      [23] P. Horton and K. Nakai, “Better prediction of protein cellular localization sites with the k nearest neighbors classifier.,†Proceedings / ... International Conference on Intelligent Systems for Molecular Biology ; ISMB. International Conference on Intelligent Systems for Molecular Biology, 1997.

      [24] C. D. Manning, P. Ragahvan, and H. Schutze, An Introduction to Information Retrieval. Cambridge, England: Cambridge University Press, 2009.

  • Downloads

  • How to Cite

    Muhammad Noor Mathivanan, N., Azura Md. Ghani, N., Mohd Janor, R., & ., . (2019). E-Commerce Product Classification Using Supervised Learning Models. International Journal of Engineering & Technology, 8(1.7), 214-218. https://doi.org/10.14419/ijet.v8i1.7.25979