An Empirical Study of Big Data Classification Methods and Challenges

Big data classification has become popular for classifying important data such as healthcare, stock market, and other fields’ data for effective handling of many classical databases. In recent years, deep learning and machine learning techniques have become more reliable for such classification, which has resulted in effective results with better accuracy. In this survey, articles from around 10 years are taken for analyzing various techniques recently for big data classification, and this study helps to improve the future evolution of fine, adequate, and highly acceptable models for classification-based approaches. This survey contains the advantages and disadvantages of several methods, which aid in overcoming the challenges of building new ensemble models for classification. Moreover, this research provides complete knowledge about the existing methods and emphasizes the ideas for creating high-potential models as well as paving the way for inventing a real-world application to classify big data. The performance metrics to measure the classification techniques are accuracy, sensitivity, and specificity, and also provide improvement for future classification processes.