Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Abstract

The concept of 'big data' has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommodate big data environments, we provide a comprehensive overview of the history of the evolution of big data, the foundations of machine learning, and the bottlenecks and trends of machine learning in the big data era. More specifically, based on learning principals, we discuss regularization to enhance generalization. The challenges of quality in big data are reduced to the curse of dimensionality, class imbalances, concept drift and label noise, and the underlying reasons and mainstream methodologies to address these challenges are introduced. Learning model development has been driven by domain specifics, dataset complexities, and the presence or absence of human involvement. In this paper, we propose a robust learning paradigm by aggregating the aforementioned factors. Over the next few decades, we believe that these perspectives will lead to novel ideas and encourage more studies aimed at incorporating knowledge and establishing data-driven learning systems that involve both data quality considerations and human interactions.

Machine Learning Approaches in Bioengineering for Biosignal Processing

“…Machine learning can be effective in biosignal processing for several reasons. First, machine learning can handle complex and noisy data that are often encountered in biosignal processing [10]. Second, machine learning can discover hidden patterns and features that are not obvious or known beforehand [11].…”

On the improvement of reinforcement active learning with the involvement of cross entropy to address one-shot learning problem

“…However, when large-scale datasets are used for complicated tasks, complete and perfect annotations are no longer viable, due to the reality that labeling process for these datasets is labor-intensive, costly in terms of time and money, and dependent on domain experience. With the increase of dataset volume, the learning system tends to generalize better, but the cost of annotation dramatically increases [2]. Meanwhile, former studies have revealed that obtaining the ground truth label of a dataset not only requires the participation of a large number of experts in the field, but also takes more than 10 times longer to label the instance as to collect it [3].…”