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Abstract

Learning from experience dwells naturally in humans. The data analytics technology that teaches computers to accomplish the same is precisely machine learning. Undoubtedly the logical foundations of cognition predominantly support classical machine learning conversely, deep learning remains a radical departure from classical methods. As technology is advancing, the focus preliminarily remains that human replacement must be able to engineer machine learning algorithm adeptly. In order to accomplish the aforesaid, researchers need to choose the input features with caution, necessarily pre-process the data, and moreover present it proficiently. Here feature engineering has a role, which transforms the raw inputs into features with assistance from human experts. As researchers want lesser human intervention, we have suggested implementing an automatic feature extraction with deep learning in certain projects. In most of the discussed papers in order to predict human behavior related outcomes classical methods have already been employed. Also, we have carried out an analysis on the feature extraction on labelled and unlabeled data.

Keywords

Data analytics Machine learning Deep learning Features Feature engineering Automatic feature extraction Labelled data Unlabeled data 

Notes

Acknowledgements

The authors are thankful to the Faculty of School of Engineering Sciences and Technology, Jamia Hamdard for their support and cooperation throughout the making of this paper.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Engineering Sciences and TechnologyJamia HamdardNew DelhiIndia

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