Abstract
Business analytics (BA) refers to the process of organizing, processing, and examining business data for the purpose of gaining useful information that can be used to resolve problems and enhance the efficiency, productivity, and revenue. Applications of machine learning (ML) within BA have proliferated in recent years and have revolutionized the process of business decision-making, despite concerns that implementation of BA functions could lead to job loss. This chapter describes the different ML techniques being currently used in business. Several case studies in which ML is used for business purposes are presented. Open research problems in the areas of ML in BA are discussed.
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References
6 examples of AI in business intelligence applications. (n.d.). https://emerj.com/ai-sector-overviews/ai-in-business-intelligence-applications/. Accessed online on 2 July 2020.
Abbasi, A., & Chen, H. (2008). CyberGate: A system and design framework for text analysis of computer mediated communication. MIS Quarterly, 32(4), 811–837.
Abiodun, O., Jantan, A., Omolara, O., Dada, K., Mohamed, N., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4, e00938. https://doi.org/10.1016/j.heliyon.2018.e00938
Agarwal, A., Chakrabarti S., & Aggarwal, S. (2006). Learning to rank networked entities. In Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining (pp. 14–23).
Al Hamad, M., & Zeki, A. M. (2018). Accuracy vs. cost in decision trees: A survey. In 2018 international conference on innovation and intelligence for informatics, computing, and technologies (3ICT), Sakhier (pp. 1–4). https://doi.org/10.1109/3ICT.2018.8855780.
Al-Aidaroos, K. M., Bakar, A. A., & Othman, Z. (2010). Naïve bayes variants in classification learning. In 2010 international conference on information retrieval & knowledge management (CAMP), Shah Alam, Selangor (pp. 276–281). https://doi.org/10.1109/INFRKM.2010.5466902.
Alam, T. (2019). Forecasting exports and imports through artificial neural network and autoregressive integrated moving average.Decision. Science Letters, 8(3), 249–260.
Archana Bai, S. (2011). Machine Learning technologies in business and engineering. In International conference on sustainable energy and intelligent systems (SEISCON 2011), Chennai (pp. 856–859). https://doi.org/10.1049/cp.2011.0486.
Better Bayesian Learning. (2003). http://paulgraham.com/better.html. Accessed 9 Jan 2021.
Bhattacharyya, S., & Bandyopadhyay, G. (2014). Comparative analysis using multinomial logistic regression. In 2014 2nd international conference on business and information management (ICBIM), Durgapur (pp. 119–124). https://doi.org/10.1109/ICBIM.2014.6970970.
Brynjolfsson, E., & Mcafee, A. (2017). The business of artificial intelligence. Harvard Business Review 4 (11), 1–41.
Chen, H., & Zimbra, D. (2010). AI and opinion mining. IEEE Intelligent Systems, 25(3), 74–76.
Chua, F. C. T., Lauw, H. W., & Lim E. -P. (2011). Predicting item adoption using social correlation. In Proceedings of the SIAM international conference on data mining (pp. 367–378).
Contractor, N. (2009). The emergence of multidimensional networks. Journal of Computer-Mediated Communication, 14, 3.
Faggella, D. (2018a, December). Furniture retailer saves time on customer support with routing and macros. AI Case Studies.
Faggella, D. (2018b, December). Smart home services provider automated report creation with AI and customer data. AI Case Studies.
Faggella, D. (2018c, December). Microsoft gets the pulse of customer sentiment with natural language processing. AI Case Studies.
Faggella, D. (2019, January). Automotive repair equipment OEM uses AI to monetize repair service data. Business Intelligence and Analytics.
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3–5), 75–174.
Gaber, M. M., Zaslavsky, A., & Krishnaswamy, S. (2005). Mining data streams: A review. SIGMOD Record, 34(2), 18–26.
Goh, R., & Lee, L. S. (2019, 2019). Credit scoring: A review on support vector machines and metaheuristic approaches. Advances in Operations Research, 1–30. https://doi.org/10.1155/2019/1974794
Guha, R., McCool, R., & Miller E. (2003). Semantic search. In Proceedings of the international world wide web conference.
Guo-sheng, H., & Guo-hong, Z. (2007). The study of credit evaluation of business websites using support vector machines. In 2007 international conference on management science and engineering, Harbin (pp. 263–267). https://doi.org/10.1109/ICMSE.2007.4421858.
Gupta, A., Salau, A. O., Chaturvedi, P., Akinola, S. A., & Ikechi Nwulu, N. (2019). Artificial neural networks: Its techniques and applications to forecasting. In 2019 international conference on automation, computational and technology management (ICACTM), London (pp. 320–324). https://doi.org/10.1109/ICACTM.2019.8776701.
Haifley, T. (2002). Linear logistic regression: An introduction. In IEEE international integrated reliability workshop final report, 2002, Lake Tahoe (pp. 184–187). https://doi.org/10.1109/IRWS.2002.1194264.
Henschen, D. (2011). Why all the Hadoopla? Information Week, 11(14/11), 19–26.
Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval (pp. 230–237).
Hui-lin, Q., & Feng, G. (2011). A research on logistic regression model based corporate credit rating. In 2011 international conference on E-Business and E-Government (ICEE), Shanghai (pp. 1–4). https://doi.org/10.1109/ICEBEG.2011.5882285.
IBM. (2011, November15). The 2011 IBM tech trends report. http://ibm.com/developerworks/techntrendsreport. Accessed online on 10 July 2020.
Jiang, L., Cai, Z., Wang, D., & Jiang, S. (2007). Survey of improving K-nearest-neighbor for classification. In Fourth international conference on fuzzy systems and knowledge discovery (FSKD 2007), Haikou (pp. 679–683). https://doi.org/10.1109/FSKD.2007.552.
Jinguo, X., & Chen, X. (2011). Application of decision tree method in economic statistical data processing. In 2011 international conference on E-Business and E-Government (ICEE), Shanghai (pp. 1–4). https://doi.org/10.1109/ICEBEG.2011.5887040.
Kang, U., Tsourakakis, C. E., Appel, A. P., Faloutsos, C., & Leskovec, J. (2011). HADI: Mining radii of large graphs. ACM Transactions on Knowledge Discovery from Data, 5(2), 1–24.
Karamizadeh, S., Abdullah, S. M., Halimi, M., Shayan, J., & Rajabi, M. J. (2014). Advantage and drawback of support vector machine functionality. In 2014 international conference on computer, communications, and control technology (I4CT), Langkawi (pp. 63–65). https://doi.org/10.1109/I4CT.2014.6914146.
Kotsiantis, S. B. (2013). Decision trees: A recent overview. Artificial Intelligence Review, 39, 261. https://doi.org/10.1007/s10462-011-9272-4
Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7), 1019–1031.
Lim, E. P., Chen, H., & Chen, G. (2013). Business intelligence and analytics: Research directions. ACM Transactions on Management Information Systems, 3(4), 1–10. Research Collection School of Information Systems.
Lin, J., Snow, R., & Morgan, W. 2011. Smoothing techniques for adaptive online language models: Topic tracking in tweet streams. In Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining.
Lovell, B., & Walder, C. (2006). Support vector machines for business applications. In Mathematical methods for knowledge discovery and data mining. https://doi.org/10.4018/978-1-59904-528-3.ch005
Ma, H., Yang, H., Lyu, M. R., & King, I. 2008. Sorec: Social recommendation using probabilistic matrix factorization. In Proceedings of the ACM conference on information and knowledge management (pp. 931–940).
Martinez-Arroyo, M., & Sucar, L. E. (2006). Learning an optimal naive bayes classifier. In 18th international conference on pattern recognition (ICPR'06), Hong Kong (pp. 1236–1239). https://doi.org/10.1109/ICPR.2006.748.
Mishra, M., & Srivastava, M. (2014). A view of artificial neural network. In 2014 international conference on advances in engineering & technology research (ICAETR - 2014), Unnao (pp. 1–3). https://doi.org/10.1109/ICAETR.2014.7012785.
Narula, G. (2019). Machine learning algorithms for business applications – complete guide. Business Intelligence and Analytics.
Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the Web. http://dbpubs.stanford.edu/pub/1999-66.
Parseh, M., Rahmanimanesh, M., & Keshavarzi, P. (2020). Persian handwritten digit recognition using combination of convolutional neural network and support vector machine methods. The International Arab Journal of Information Technology, 17, 572–578. https://doi.org/10.34028/iajit/17/4/16
Porter, M. A., Onnela, J.-P., & Mucha, P. J. (2009). Communities in networks. Notice of AMS, 56(9), 1082–1097.
Savage, N. (2012). Gaining wisdom from crowds. Communications of the ACM, 55(3), 13–15.
Shalizi, C. R., & Thomas, A. C. (2011). Homophily and contagion are generically confounded in observational social network studies. Sociological Methods & Research, 40, 211–239.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263–286.
Sun, Y., & Han, J. (2012). Mining heterogeneous information networks: Principles and methodologies. Morgan & Claypool Publishers.
Trippi, R. R., & Turban, E. (1992). Neural networks in finance and investing: Using Machine Learning to improve real-world performance. McGraw-Hill, Inc.
Vasundhara, D. N., & Seetha, M. (2016). Rough-set and artificial neural networks-based image classification. In 2016 2nd international conference on contemporary computing and informatics (IC3I), Noida (pp. 35–39). https://doi.org/10.1109/IC3I.2016.7917931.
Xie, T., Yu, H., & Wilamowski, B. (2011). Comparison between traditional neural networks and radial basis function networks. In 2011 IEEE international symposium on industrial electronics, Gdansk (pp. 1194–1199). https://doi.org/10.1109/ISIE.2011.5984328.
Yu, X., Qi, Z., & Zhao, Y. (2013). Support vector regression for newspaper/magazine sales forecasting. Procedia Computer Science, 17, 1055–1062. https://doi.org/10.1016/j.procs.2013.05.134
Zahrani, A., & Marghalani, A. (2018). How artificial intelligent transform business? https://doi.org/10.13140/RG.2.2.20426.67522
Zekic-Susac, M., Sarlija, N., & Bensic, M. (2004). Small business credit scoring: A comparison of logistic regression, neural network, and decision tree models. In 26th international conference on information technology interfaces, 2004, Cavtat (pp. 265–270, Vol. 1).
Zhong, Y. (2016). The analysis of cases based on decision tree. In 2016 7th IEEE international conference on software engineering and service science (ICSESS), Beijing (pp. 142–147). https://doi.org/10.1109/ICSESS.2016.7883035.
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Shastry, K.A., Sanjay, H.A., Sushma, V. (2022). Machine Learning for Business Analytics: Case Studies and Open Research Problems. In: Alloghani, M., Thron, C., Subair, S. (eds) Artificial Intelligence for Data Science in Theory and Practice. Studies in Computational Intelligence, vol 1006. Springer, Cham. https://doi.org/10.1007/978-3-030-92245-0_1
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DOI: https://doi.org/10.1007/978-3-030-92245-0_1
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