Smart Fraud Detection in E-Commerce Using Machine Learning

Abstract

Machine Learning is an area of focus within Artificial Intelligence which has received considerable attention as part of the solutions to the digital transformation problem in the modern world. This paper attempts to review some of the most popular and frequently used machine learning algorithms. The author seeks to assist in decision making by highlighting the advantages and disadvantages of machine learning algorithms withrespect toapplicationsso that appropriate decisionscanbemadewithrespecttothechosenalgorithm for the specific requirements of the application.

Introduction

Machine Learning (ML) enables computers to improve performance on tasks over time through experience. It’s widely used in fields such as healthcare, virtual assistants, fraud detection, recommendation systems, and more.

Key ML Algorithms and Concepts

1. Gradient Descent

An optimization method used to minimize cost functions in ML models. There are three types:

• Batch Gradient Descent (BGD): Computes the gradient using the entire dataset (stable but slower).

• Stochastic Gradient Descent (SGD): Updates model using one data point at a time (faster, but noisier).

• Mini-Batch Gradient Descent (MBGD): A compromise using small subsets (balances speed and stability).

2. Linear Regression

A supervised learning algorithm used to predict a continuous outcome based on a linear relationship between input (X) and output (Y). It's simple, efficient, and useful for tasks like real estate pricing and sales forecasting but may oversimplify complex problems.

3. Multivariate Regression

Expands linear regression to include multiple input variables. It models real-world complexities better but may introduce multicollinearity (inter-correlation among inputs), requiring large datasets and careful statistical understanding.

4. Logistic Regression

Used for classification tasks with binary, multinomial, or ordinal outcomes (e.g., spam detection or disease classification). It's simple, fast, and effective but performs poorly on non-linear problems.

5. Support Vector Machine (SVM)

A powerful algorithm for both classification and regression. It uses hyperplanes to separate data and kernel functions to handle non-linearity. It works well in high-dimensional spaces but can be slow on large datasets and difficult to interpret.

6. Naive Bayes

A probabilistic classifier based on Bayes’ theorem with an assumption of feature independence. It’s simple, fast, and effective for text classification and medical predictions, but less accurate than more complex models and doesn’t scale well with many classes.

7. K-Nearest Neighbors (KNN)

A non-parametric, instance-based algorithm that classifies data based on proximity to labeled examples. It’s intuitive and effective for certain applications but computationally intensive and sensitive to irrelevant features and noise.

8. K-Means Clustering

An unsupervised learning algorithm used for grouping data into k clusters based on similarity. It’s efficient and widely used in applications like customer segmentation and document classification. However, it’s sensitive to outliers and initial conditions, and selecting the right number of clusters (k) can be challenging.

Conclusion

Inthispaper,wetakeacloserlookatthemostcommonlyused machine learning algorithms designed to tackle classification, regression, and clustering challenges. We dive into the pros and cons of these algorithms, comparing their performanceand learning rates wherever possible. Additionally, we highlight real-world applications of these techniques. We also explore various types of machine learning methods, including supervised, unsupervised, and semi-supervised learning. Our goal is to provide readers with valuable insights that will help them make informed decisions when it comes to choosing the right machine learning algorithm for their specific problem- solving needs.

References

[1] D. Pelleg, A. Moore (2000): “X-means: Extending K-means withEfficientEstimationoftheNumberofClusters”; ICML’00 Proceedings of the Seventeenth International Conference on Machine Learning, pp.727-734. [2] RushikaGhadge, JuileeKulkarni, Pooja More, Sachee Nene, Priya R , [3]“Prediction of Crop Yield using Machine Learning”,International Research Journal of Engineering & Technology, Vol 5, Issue 2, Feb2018. [3] C. Phua, V.Lee, K. Smith, R. Gayler (2010);“Comprehensive Survey of Data Mining-based Fraud Detection Research”, ICICTA’10Proceedings ofthe2010International Conference on Intelligent Computation Technology and Automation Volume 1, pp.50-53. [4] S. Cheng, J. Liu, X. Tang (2014); “Using unlabeled Data to Improve Inductive Models by Incorporating Transductive Models”; International Journal of Advanced Research in Artificial Intelligence, Volume 3 Number 2, pp.33-38. [5] Sonal S. Ambalkar, S. S. Thorat2, “Bone Tumor Detection from MRI Images using Machine Learning: A Review”, InternationalResearchJournalofEngineering&Technology”, Vol. 5, Issue 1, Jan -2018. [6] RajatRaina, Alexis Battele, HonglakLee,BenjaminPacker, Andrew Y. Ng , “Self-taught Learning : Transfer of Learning from Unlabeled Data”, Computer Science Department, Stanford University, CA, USA, Proceedings of 24th International Conference on Machine Learning Corvallis, OR, 2007. [7] Jimmy Lin, AlekKolcz, “Large-Scale Machine Learning at Twitter”, Proceedings of SIGMOD ’12, May 20–24, 2012, Scottsdale, Arizona, USA. [8] Dr. Rama Kishore, TaranjitKaur, “Backpropagation Algorithm:AnArtificialNeuralNetworkApproachforPatternRecognition”,InternationalJournalofScientific& Engineering Research, Volume 3, Issue 6, June-2012. [9] KedarPotdar, RishabKinnerkar, “A Comparative Study of MachineAlgorithmsappliedtoPredictiveBreastCancer Data”, International Journal ofScience & Research, Vol. 5, Issue 9, pp. 1550-1553, September 2016.

Copyright

Copyright © 2025 K. Dasarathi Shohi, R. Monika , K. Charumathi, M. Aarthi, K. Harini. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.