Abstract
In spite of the prominence of extreme learning machine model, as well as its excellent features such as insignificant intervention for learning and model tuning, the simplicity of implementation, and high learning speed, which makes it a fascinating alternative method for Artificial Intelligence, including Big Data Analytics, it is still limited in certain aspects. These aspects must be treated to achieve an effective and cost-sensitive model. This review discussed the major drawbacks of ELM, which include difficulty in determination of hidden layer structure, prediction instability and Imbalanced data distributions, the poor capability of sample structure preserving (SSP), and difficulty in accommodating lateral inhibition by direct random feature mapping. Other drawbacks include multi-graph complexity, global memory size, one-by-one or chuck-by-chuck (a block of data), global memory size limitation, and challenges with big data. The recent trend proposed by experts for each drawback is discussed in detail towards achieving an effective and cost-sensitive model.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.