A Framework Using Binary Cross Entropy - Gradient Boost Hybrid Ensemble Classifier for Imbalanced Data Classification

During the big data era, there is a continuous occurrence of developing the learning of imbalanced data gives a pathway for the research field along with data mining and machine learning concepts. In recent years, Big Data and Big Data Analytics having high eminence due to data exploration by many of the applications in real-time. Using machine learning will be a greater solution to solve the difficulties that occur when we learn the imbalanced data. Many real-world applications have to predict the solutions for highly imbalanced datasets with the imbalanced target variable. In most of the cases, the target variable assigns or having the least occurrences of the target values due to the sort of imbalances associated with things or events strongly applicable for the users who avail the solutions (for example, results of stock changes, fraud finding, network security, etc.). The expansion of the availability of data due to the rise of big data from the network systems such as security, internet transactions, finance manipulations, surveillance of CCTV or other devices makes the chance to the critical study of insufficient knowledge from the imbalance data when supporting the decision making processes. The data imbalance occurrence is a challenge to the research field. In recent trends, there is more data level and an algorithm level method is being upgraded constantly and leads to develop a new hybrid framework to solve this problem in classification. Classifying the imbalanced data is a challenging task in the field of big data analytics. This study mainly concentrates on the problem existing in most cases of real-world applications as an imbalance occurs in the data. This difficulty present due to the data distribution with skewed nature. We have analyses the data imbalance and find the solution. This paper concentrates mainly on finding a better solution to this nature of the problem to be solved with the proposed framework using a hybrid ensemble classifier based on the Binary Cross-Entropy method as loss function along with the Gradient Boost Algorithm.