Predicting Personality Using Deep Learning Techniques
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.
KeywordsData analytics Machine learning Deep learning Features Feature engineering Automatic feature extraction Labelled data Unlabeled data
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.
- 1.En.wikipedia.org: Functional integration (neurobiology) (2017)Google Scholar
- 2.Deng, L.: An overview of deep-structured learning for information processing (2018). https://www.microsoft.com/en-us/research/publication/an-overview-of-deep-structured-learning-for-information-processing/
- 3.Silver, D., Huang, A., Maddison, C., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)CrossRefGoogle Scholar
- 4.Kanter, J., Veeramachaneni, K.: Deep feature synthesis: towards automating data science endeavors. In: IEEE International Conference on Data Science and Advanced Analytics (DSAA) (2015)Google Scholar
- 6.Heaton, J.: An empirical analysis of feature engineering for predictive modeling. https://arxiv.org/abs/1701.07852. Accessed 30 Apr 2018
- 8.Coates, A., Ng, A., Lee, H.: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR, vol. 15, pp. 215–223 (2011)Google Scholar
- 12.Keshtkar, F., Burkett, C., Li, H., Graesser, A.C.: Using data mining techniques to detect the personality of players in an educational game (2013)Google Scholar
- 13.Bijalwan, V., Balodhi, M., Gusain, A.: Human emotion recognition using thermal image processing and eigenfaces (2015)Google Scholar
- 14.Golbeck, J., Robles, C., Turner, K.: Predicting personality with social media. In: Proceedings of the 2011 Annual Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA 2011 (2011)Google Scholar