A Deep Learning Application for Automated Feature Extraction in Transaction-based Machine Learning |
Woo, Deock-Chae
(국민대학교 데이터사이언스학과)
Moon, Hyun Sil (경희대학교 경영대학 & AI경영연구센터) Kwon, Suhnbeom (국민대학교 경영학부) Cho, Yoonho (국민대학교 경영학부) |
1 | Ahn, S.M., "Deep Learning Architectures and Applications", Journal of Intelligence and Information Systems, Vol.22, No.2, 2016, 127-142. DOI |
2 | Alex, S., S.H. Seo, and Y. Kwon, "Development of Deep Learning Models for Multi-class Sentiment Analysis", Journal of Information Technology Services, Vol.16, No.4, 2017, 149-160. DOI |
3 | Babaee, M., D.T. Dinh, and G. Rigoll, "A deep convolutional neural network for video sequence background subtraction", Pattern Recognition, Vol.76, 2018, 635-649. DOI |
4 | Chollet, F., Deep Learning with Python, Manning Publications Company, New York, 2017. |
5 | Balaji, A. and A. Allen, "Benchmarking Automatic Machine Learning Frameworks", arXiv preprint arXiv:1808.06492, 2018. |
6 | Bansal, T., D. Belanger, and A. McCallum, "Ask the gru : Multi-task learning for deep text recommendations", In Proceedings of the 10th ACM Conference on Recommender Systems, 2016, 107-114. |
7 | Barkan, O. and N. Koenigstein, "Item2vec : neural item embedding for collaborative filtering", In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing, 2016, 1-6. |
8 | Kanter, J.M. and K. Veeramachaneni, "Deep feature synthesis : Towards automating data science endeavors", In 2015 IEEE International Conference on Data Science and Advanced Analytics, 2015, 1-10. |
9 | Tibshirani, R.J., "Statistical Learning with Big Data", In the Joint Statistical Meetings 2017, 2017. |
10 | Sun, Z., J. Yang, J. Zhang, A. Bozzon, Y. Chen, and C. Xu, "MRLR : Multi-level Representation Learning for Personalized Ranking in Recommendation", In 26th International Joint conferences on Artificial Intelligence, 2017, 2807-2813. |
11 | Thomas, R., An Introduction to Deep Learning for Tabular Data, 2018. Available at https://www.fast.ai/2018/04/29/categorical-embeddings(Downloaded 28 February, 2019) |
12 | Wallach, H.M., "Topic modeling : beyond bag-ofwords", In Proceedings of the 23rd International Conference on Machine Learning, 2006, 977-984. |
13 | Pembeci, I., "Using word embeddings for ontology enrichment", International Journal of Intelligent Systems and Applications in Engineering, Vol.4, No.3, 2016, 49-56. DOI |
14 | Wang, Y., L. Kung, and T.A. Byrd, "Big data analytics : Understanding its capabilities and potential benefits for healthcare organizations", Technological Forecasting and Social Change, Vol.126, 2018, 3-13. DOI |
15 | Wang, Y. and X.J. Wang, "A new approach to feature selection in text classification", In 2005 International conference on machine learning and cybernetics, 2005, 3814-3819. |
16 | Wu, L., S.C. Hoi, and N. Yu, "Semantics-preserving bag-of-words models and applications, "IEEE Transactions on Image Processing", Vol.19, No.7, 2010, 1908-1920. DOI |
17 | Zhang, D., H. Xu, Z., Su, and Y. Xu, "Chinese comments sentiment classification based on word2vec and SVMperf", Expert Systems with Applications, Vol.42, No.4, 2015, 1857-1863. DOI |
18 | Zhang, Y., R. Jin, and Z.H., Zhou, "Understanding bag-of-words model : a statistical framework", International Journal of Machine Learning and Cybernetics, Vol.1, No.1-4, 2010, 43-52. DOI |
19 | Katz, G., E.C.R. Shin, and D. Song, "Explorekit : Automatic feature generation and selection", In 2016 IEEE 16th International Conference on Data Mining, 2016, 979-984. |
20 | Bradley, A.P., "The use of the area under the ROC curve in the evaluation of machine learning algorithms", Pattern recognition, Vol.30, No.7, 1997, 1145-1159. DOI |
21 | Kohavi, R., "A study of cross-validation and bootstrap for accuracy estimation and model selection", In the International Joint Conference on Artificial Intelligence, Vol.14, No.2, 1995, 1137-1145. |
22 | Krizhevsky, A., I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks", In Advances in neural information processing systems, 2012, 1097-1105. |
23 | Lam, H.T., J.M. Thiebaut, M. Sinn, B. Chen, T. Mai, and O. Alkan, "One button machine for automating feature engineering in relational databases", arXiv preprint arXiv : 1706.00327, 2017. |
24 | LaValle, S., E. Lesser, R. Shockley, M.S., Hopkins, and N. Kruschwitz, "Big data, analytics and the path from insights to value", MIT Sloan Management Review, Vol.52, No.2, 2011, 21-31. |
25 | Lee, H., D. Lim, and H. Zo, "Personal Information Overload and User Resistance in the Big Data Age", Journal of Intelligence and Information Systems, Vol.19, No.1, 2013, 125-139. DOI |
26 | Lee, J.J., S.B. Kwon, and S.M. Ahn, "Sementic Analysis Using Deep Learning Model based on Phoneme-level Korean", Journal of Information Technology Services, Vol.17, No.1, 2018, 77-89. |
27 | Deng, L. and Y. Liu, Deep Learning in Natural Language Processing, Springer, Singapore, 2018. |
28 | Chen, T. and C. Guestrin, "Xgboost : A scalable tree boosting system", In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, 785-794. |
29 | Cho, K., B. Van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, "Learning phrase representations using RNN encoder-decoder for statistical machine translation", arXiv preprint arXiv : 1406.1078, 2014. |
30 | Chung, J., C. Gulcehre, K. Cho, and Y. Bengio, "Empirical evaluation of gated recurrent neural networks on sequence modeling", arXiv preprint arXiv : 1412.3555, 2014. |
31 | Dhingra, B., H. Liu, Z. Yang, W.W. Cohen, and R. Salakhutdinov, "Gated-attention readers for text comprehension", arXiv preprint arXiv : 1606.01549, 2016. |
32 | Domingos, P.M., "A few useful things to know about machine learning", Communications of the ACM, Vol.55, No.10, 2012, 78-87. DOI |
33 | Faust, O., Y. Hagiwara, T.J. Hong, O.S. Lih, and U.R. Acharya, "Deep learning for healthcare applications based on physiological signals : a review", Computer methods and programs in biomedicine , Vol.161, 2018, 1-13. DOI |
34 | Ghosh, S. and M.S. Desarkar, "Class Specific TF-IDF Boosting for Short-text Classification : Application to Short-texts Generated During Disasters", In Companion of the The Web Conference 2018 on The Web Conference 2018, 2018, 1629-1637. |
35 | Barnaghi, P., A. Sheth, and C. Henson, "From data to actionable knowledge : big data challenges in the web of things", IEEE Intelligent Systems, Vol.6, 2013, 6-11. DOI |
36 | Ozsoy, M.G., "From word embeddings to item recommendation", arXiv preprint arXiv : 1601.01356, 2016. |
37 | Mitchell, T.M. Machine Learning, McGraw-Hill, New York, 1997. |
38 | Mikolov, T., I. Sutskever, K., Chen, G.S. Corrado, and J. Dean, "Distributed representations of words and phrases and their compositionality", In Advances in neural information processing systems, 2013, 3111-3119. |
39 | Muller, A.C. and S. Guido, Introduction to machine learning with Python : a guide for data scientists, O'Reilly Media, Inc., California, 2016. |
40 | Ng., A., Machine Learning and AI via brain simulations, 2013, Available at http://datascien ceassn.org/sites/default/files/Machine%20 Learning%20and%20AI%20via%20Brain% 20Simulations.pdf(Downloaded 28 February, 2019). |
41 | Pal, N.R. and S.K. Pal, "A review on image segmentation techniques", Pattern recognition, Vol.26, No.9, 1993, 1277-1294. DOI |
42 | Park, C.Y., I.H., Jang, and Z.K. Lee, "Authorship Attribution of Web Texts with Korean Language Applying Deep Learning Method", Journal of Information Technology Services, Vol.15, No.3, 2016, 147-155. DOI |
43 | Park, J. and Y. Cho, "Clickstream Big Data Mining for Demographics based Digital Marketing", Journal of Intelligence and Information Systems, Vol.22, No.3, 2016, 143-163. DOI |
44 | Rusinol, M. and J. Llados, "Logo spotting by a bag-of-words approach for document categorization", In 2009 10th international conference on document analysis and recognition, 2009, 111-115. |
45 | Hochreiter, S. and J. Schmidhuber, "Long shortterm memory", Neural computation, Vol.9, No.8, 1997, 1735-1780. DOI |
46 | Zheng, A. and A. Casari, Feature Engineering for Machine Learning : Principles and Techniques for Data Scientists, O'Reilly Media, Inc., California, 2018. |
47 | Zhou, P., Z. Qi, S., Zheng, J., Xu, H., Bao, and B., Xu, "Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling", arXiv preprint arXiv : 1611.06639, 2016. |
48 | Zhou, Q., N. Yang, F. Wei, C. Tan, H. Bao, and M. Zhou, "Neural question generation from text : A preliminary study", In National CCF Conference on Natural Language Processing and Chinese Computing, 2017, 662-671. |
49 | Hanley, J.A. and B.J. McNeil, "The meaning and use of the area under a receiver operating characteristic(ROC) curve", Radiology, Vol. 143, No.1, 1982, 29-36. DOI |
50 | He, K., X. Zhang, S. Ren, and J. Sun, "Delving deep into rectifiers : Surpassing humanlevel performance on imagenet classification", In Proceedings of the IEEE international conference on computer vision, 2015, 1026-1034. |
51 | Goodfellow, I., Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016. |
52 | IBM, Extracting business value from the 4 V's of big data, 2017, Available at https://www.ibmbigdatahub.com/infographic/extracting-business-value-4-vs-big-data(Downloaded 28 February, 2019) |
53 | Jaderberg, M., A. Vedaldi, and A. Zisserman, "Deep features for text spotting", In European conference on computer vision, 2014, 512-528. |
54 | Johnson, R. and T. Zhang, "Effective use of word order for text categorization with convolutional neural networks", arXiv preprint arXiv : 1412.1058, 2014. |
55 | Jordan, M.I. and T.M. Mitchell, "Machine learning : Trends, perspectives, and prospects", Science , Vol.349, No.6245, 2015, 255-260. DOI |
56 | Joulin, A., E. Grave, P. Bojanowski, and T. Mikolov, "Bag of tricks for efficient text classification", arXiv preprint arXiv : 1607.01759, 2016. |
57 | Jozefowicz, R., W. Zaremba, and I. Sutskever, "An empirical exploration of recurrent network architectures", In International Conference on Machine Learning, 2015, 2342-2350. |
58 | Sikka, K., T. Wu, J., Susskind, and M. Bartlett, "Exploring bag of words architectures in the facial expression domain", In European Conference on Computer Vision, 2012, 250-259. |
59 | Sarkar, D.J. Understanding Feature Engineering (Part 1)-Continuous Numeric Data, 2018, Available at https://towardsdatascience.com/understanding-feature-engineering-part-1-continuous-numeric-data-da4e47099a7b (Downloaded 28 February, 2019) |
60 | Sharif Razavian, A., H. Azizpour, J. Sullivan, and S. Carlsson, "CNN features off-the-shelf: an astounding baseline for recognition", In Proceedings of the IEEE Conference On Computer Vision and Pattern Recognition Workshops, 2014, 806-813. |
61 | Snoek, J., H. Larochelle, and R.P. Adams, "Practical bayesian optimization of machine learning algorithms", In Advances in Neural Information Processing Systems, 2012, 2951-2959. |