State and Trends of Machine Learning Approaches in Business: An Empirical Review
Lecture Notes on Data Engineering and Communications Technologies, ISSN: 2367-4520, Vol: 43, Page: 1-16
2020
- 1Citations
- 11Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Book Chapter Description
Strong competition is imposing to enterprises an incessant need for extracting more business values from collected data. The business value of contemporary volatile data derives from the meanings mainly for market tendencies, and overall customer behaviors. With such continuous urge to mine valuable patterns from data, analytics have skipped to the top of research topics. One main solution for the analysis in such context is ‘Machine Learning’ (ML). However, Machine Learning approaches and heuristics are plenty, and most of them require outward knowledge and deep thoughtful of the context to learn the tools fittingly. Furthermore, application of prediction in business has certain considerations that strongly affects the effectiveness of ML techniques such as noisy, criticality, and inaccuracy of business data due to human involvement in an extensive number of business tasks. The objective of this paper is to inform about the trends and research trajectory of Machine Learning approaches in business field. Understanding the vantages and advantages of these methods can aid in selecting the suitable technique for a specific application in advance. The paper presents a comprehensively review of the most relevant academic publications in the topic carrying out a review methodology based on imbricated nomenclatures. The findings can orient and guide academics and industrials in their applications within business applications.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85083455680&origin=inward; http://dx.doi.org/10.1007/978-3-030-36178-5_1; http://link.springer.com/10.1007/978-3-030-36178-5_1; http://link.springer.com/content/pdf/10.1007/978-3-030-36178-5_1; https://dx.doi.org/10.1007/978-3-030-36178-5_1; https://link.springer.com/chapter/10.1007/978-3-030-36178-5_1
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know