Classification of Imbalanced Data in E-Commerce Using Machine Learning

Description

Unusual instances of data, called imbalanced data, are still being ignored at large because of the inadequacies of analytical methods that are designed to handle homogenized data sets and to “smooth out” outliers.
In the past decade, a myriad of approaches handling this problem that range from data modifications to alterations of existing algorithms have appeared with varying success. Within the vast domain of e-Commerce, we are proposing a new approach for handling imbalanced data, which is a hybrid classification method that will consist of a mixed solution of multi-modal data formats and algorithmic adaptations for an optimal balance between prediction accuracy, precision and specificity.Our solution improves data usability, classification accuracy and resulting costs of analyzing massive data sets used with the Logistic Regression and Convolutional Neural Network algorithms for classification.