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Strategic Feature Selection Techniques for E-Commerce Application and Data Protection in AI and ML
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Author(s): Vipin Kumar (Lovely Professional University, India), Mohammad Suleman Quraishi (Acharya University, Uzbekistan)and Vipin Kumar Chaudhary (Lovely Professional University, India)
Copyright: 2025
Pages: 26
Source title:
Strategic Innovations of AI and ML for E-Commerce Data Security
Source Author(s)/Editor(s): Gaganpreet Kaur (Chitkara University, India), Jatin Arora (Chitkara University, India), Vishal Jain (Sharda University, Greater Noida, India)and Asadullah Shaikh (Najran University, Saudi Arabia)
DOI: 10.4018/979-8-3693-5718-7.ch014
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Abstract
E-commerce applications are widely used in shopping, money transfers, and other purposes. These applications store the user data to provide good results and recommend new products to the user. The user data saved on the server are critical and must provide proper security, safety, and availability. Any attack on these data may damage users' privacy. Machine learning can analyze and detect any attack on these data and applications. A machine learning model uses high-impact features to make a good model and effective prediction. Selecting the most critical data attributes is known as feature selection. Effective feature selection is a vital component in the process of developing a model with a high level of accuracy. This chapter explains the feature selection process and compares different feature selection techniques. This chapter analyzes filter-matched, wrapper-matched, and embedded methods and their variations. This chapter discussed the essential features concerning the security of e-commerce applications and e-commerce data.
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