Efficient Machine Learning for Big Data: A Review

By Omar Y. Al-Jarrah, Paul Yoo, Sami Hakam Muhaidat, George K. Karagiannidis, K. Taha. Big Data Research, 2015

614 citations recorded by the source corpus.

With the emerging technologies and all associated devices, it is predicted that massive amount of data will be created in the next few years - in fact, as much as 90% of current data were created in the last couple of years - a trend that will continue for the foreseeable future. Sustainable computing studies the process by which computer engineer/scientist designs computers and associated subsystems efficiently and effectively with minimal impact on the environment. However, current intelligent machine-learning systems are performance driven - the focus is on the predictive/classification accuracy, based on known properties learned from the training samples. For instance, most machine-learning-based nonparametric models are known to require high computational cost in order to find the global optima. With the learning task in a large dataset, the number of hidden nodes within the network will therefore increase significantly, which eventually leads to an exponential rise in computational complexity. This paper thus reviews the theoretical and experimental data-modeling literature, in large-scale data-intensive fields, relating to: (1) model efficiency, including computational requirements in learning, and data-intensive areas' structure and design, and introduces (2) new algorithmic approaches with the least memory requirements and processing to minimize computational cost, while maintaining/improving its predictive/classification accuracy and stability.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/8bcc25651b62532f639fe713da9d7b67c742d562

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In Artificial Intelligence

Citations in the corpus

The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations (2021, background): “In the short term, the training phase is computationally more demanding and energy intensive (Al-Jarrah et al. 2015).”
Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing (2020, background): “With the increased access to artificial intelligence (AI) development tools and Internet-sourced datasets, corporations, nonprofits and governments are deploying AI systems at an unprecedented pace, often in massive-scale production systems impacting millions if not billions of users [1].”
Federated Multi-view Learning for Private Medical Data Integration and Analysis (2021, background): “However, machine learning methods [1, 30] often require a huge amount of data for better performance, and the current circumstances put deploying or developingmedical AI applications in an extremely difficult situation.”
A systematic literature review on machine learning applications for sustainable agriculture supply chain performance (2020, methodology): “This algorithm leverages the knowledge of the crowd, employing several independent models to make a prediction and aggregates the final prediction (Al-Jarrah et al. 2015; Liakos et al. 2018).”
Machine Learning from Theory to Algorithms: An Overview (2018, methodology): “Semi supervised learning can be used with problems like classification, regression and prediction [4, 13,18].”