Features Selection as a Nash-Bargaining Solution: Applications in Online Advertising and Information Systems

Published Online:https://doi.org/10.1287/ijoc.2022.1190

References

  • Adadi A, Berrada M (2018) Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access 6:52138–52160.CrossrefGoogle Scholar
  • Agarwal B, Mittal N (2013) Optimal feature selection for sentiment analysis. Gelbukh A, ed. Internat. Conf. Intelligent Text Processing Comput. Linguistics (Springer, Berlin), 13–24.Google Scholar
  • Alaimo C, Kallinikos J (2018) Objects, metrics and practices: An inquiry into the programmatic advertising ecosystem. Schultze U, Aanestad M, Mähring M, Østerlund C, Riemer K, eds. IFIP WG 8.2 Working Conf. Inform. Systems Organ. (Springer, Cham, Switzerland), 110–123.Google Scholar
  • Aziz R, Verma C, Srivastava N (2017) Dimension reduction methods for microarray data: A review. AIMS Bioengrg. 4(2):179–197.CrossrefGoogle Scholar
  • Baesens B, Bapna R, Marsden JR, Vanthienen J, Zhao JL (2016) Transformational issues of big data and analytics in networked business. MIS Quart. 40(4):807–818.CrossrefGoogle Scholar
  • Banerjee SS, Dholakia RR (2008) Mobile advertising: Does location based advertising work? Internat. J. Mobile Marketing 3(2):68–74.Google Scholar
  • Bastani H, Bayati M (2020) Online decision making with high-dimensional covariates. Oper. Res. 68(1):276–294.LinkGoogle Scholar
  • Bennasar M, Hicks Y, Setchi R (2015) Feature selection using joint mutual information maximisation. Expert Systems Appl. 42(22):8520–8532.CrossrefGoogle Scholar
  • Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2013) A review of feature selection methods on synthetic data. Knowledge Inform. Systems 34(3):483–519.CrossrefGoogle Scholar
  • Borboudakis G, Tsamardinos I (2019) Forward-backward selection with early dropping. J. Machine Learn. Res. 20(1):276–314.Google Scholar
  • Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput. Electr. Engrg. 40(1):16–28.CrossrefGoogle Scholar
  • Charkhgard H, Eshragh A (2019) A new approach to select the best subset of predictors in linear regression modelling: Bi-objective mixed integer linear programming. ANZIAM J. 61(1):64–75.CrossrefGoogle Scholar
  • Chen H, Chiang RH, Storey VC (2012) Business intelligence and analytics: From big data to big impact. MIS Quart. 36(4):1165–1188.CrossrefGoogle Scholar
  • Chen Y, Zhao X, Jia X (2015) Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J. Selected Topics Appl. Earth Observations Remote Sensing 8(6):2381–2392.CrossrefGoogle Scholar
  • Cohen S, Dror G, Ruppin E (2007) Feature selection via coalitional game theory. Neural Comput. 19(7):1939–1961.CrossrefGoogle Scholar
  • Cole R, Gkatzelis V (2018) Approximating the Nash social welfare with indivisible items. SIAM J. Comput. 47(3):1211–1236.CrossrefGoogle Scholar
  • De Mol C, De Vito E, Rosasco L (2009) Elastic-net regularization in learning theory. J. Complexity 25(2):201–230.CrossrefGoogle Scholar
  • Desboulets LDD (2018) A review on variable selection in regression analysis. Econometrics 6(4):1–27.CrossrefGoogle Scholar
  • Došilović FK, Brčić M, Hlupić N (2018) Explainable artificial intelligence: A survey. Skala K, Koricic M, Grbac TG, Cicin-Sain M, Sruk V, Ribaric S, Gros S, et al., eds. 2018 41st Internat. Convention Inform. Comm. Tech. Electronics Microelectronics (IEEE, Piscataway, NJ), 210–215. Google Scholar
  • Drummond C, Holte RC (2003) C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. Internat. Conf. Machine Learn. (ICML 2003) Workshop on Learning from Imbalanced Data Sets II, Washington, DC vol. 11 (Citeseer), 1–8.Google Scholar
  • Egea S, Mañez AR, Carro B, Sánchez-Esguevillas A, Lloret J (2017) Intelligent IoT traffic classification using novel search strategy for fast-based-correlation feature selection in industrial environments. IEEE Internet Things J. 5(3):1616–1624.CrossrefGoogle Scholar
  • Fan J, Li R (2006) Statistical challenges with high dimensionality: Feature selection in knowledge discovery. Preprint, submitted February 7, https://arxiv.org/abs/math/0602133.Google Scholar
  • Fan J, Lv J (2008) Sure independence screening for ultrahigh dimensional feature space. J. Roy. Statist. Soc. Ser. B. Statist. Methodol. 70(5):849–911.CrossrefGoogle Scholar
  • Fan J, Han F, Liu H (2014) Challenges of big data analysis. Natl. Sci. Rev. 1(2):293–314.CrossrefGoogle Scholar
  • Fischer A, Igel C (2014) Training restricted Boltzmann machines: An introduction. Pattern Recognition 47(1):25–39.CrossrefGoogle Scholar
  • Fox EJ, Hoch SJ (2005) Cherry-picking. J. Marketing 69(1):46–62.CrossrefGoogle Scholar
  • Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J. Statist. Software 33(1):1–22.CrossrefGoogle Scholar
  • Gerber E (2020) A new perspective on Shapley values, part II: The naïve Shapley method. Accessed October 29, 2020, https://edden-gerber.github.io/shapley-part-2/.Google Scholar
  • Gnana DAA, Balamurugan SAA, Leavline EJ (2016) Literature review on feature selection methods for high-dimensional data. Internat. J. Comput. Appl. 136(1):9.Google Scholar
  • Goswami S, Chakrabarti A (2014) Feature selection: A practitioner view. Internat. J. Inform. Tech. Comput. Sci. 6(11):66–77.Google Scholar
  • Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Machine Learn. 46(1–3):389–422.CrossrefGoogle Scholar
  • Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput. 18(7):1527–1554.CrossrefGoogle Scholar
  • Hsu HH, Hsieh CW, Lu MD (2011) Hybrid feature selection by combining filters and wrappers. Expert Systems Appl. 38(7):8144–8150.CrossrefGoogle Scholar
  • InMobi (2020) InMobi home page. Accessed July 22, 2020, https://www.inmobi.com/.Google Scholar
  • Janizek JD, Celik S, Lee S-I (2018) Explainable machine learning prediction of synergistic drug combinations for precision cancer medicine. Preprint, submitted May 27, https://www.biorxiv.org/content/10.1101/331769v1.article-info.Google Scholar
  • Kalai E (1977) Nonsymmetric Nash solutions and replications of 2-person bargaining. Internat. J. Game Theory 6(3):129–133.CrossrefGoogle Scholar
  • Kiang MY, Kumar A (2001) An evaluation of self-organizing map networks as a robust alternative to factor analysis in data mining applications. Inform. Systems Res. 12(2):177–194.LinkGoogle Scholar
  • Kotzias D, Denil M, De Freitas N, Smyth P (2015) From group to individual labels using deep features. Proc. 21th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 597–606.Google Scholar
  • Langley P (1994) Selection of relevant features in machine learning. Greiner R, Subramanian D, eds. Relevance: Papers AAAI Fall Sympos. (AAAI Press, Menlo Park, CA), 140–144.Google Scholar
  • Lash MT, Zhao K (2016) Early predictions of movie success: The who, what, and when of profitability. J. Management Inform. Systems 33(3):874–903. CrossrefGoogle Scholar
  • Lessmann S, Voß S (2009) Feature selection in marketing applications. Huang R, Yang Q, Pei J, Gama J, Meng X, Li X, eds. Internat. Conf. Adv. Data Mining Appl. (Springer, Berlin), 200–208.Google Scholar
  • Lilleberg J, Zhu Y, Zhang Y (2015) Support vector machines and word2vec for text classification with semantic features. 2015 IEEE 14th Internat. Conf. Cognitive Informatics Cognitive Comput. (IEEE, Piscataway, NJ), 136–140.Google Scholar
  • Liu Q, Chen C, Zhang Y, Hu Z (2011) Feature selection for support vector machines with RBF kernel. Artificial Intelligence Rev. 36(2):99–115.CrossrefGoogle Scholar
  • Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. von Luxburg U, Guyon I, Bengio S, Wallach H, Fergus R, eds. Proc. 31st Internat.Conf. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 4768–4777.Google Scholar
  • Lundberg SM, Erion GG, Lee SI (2018) Consistent individualized feature attribution for tree ensembles. Preprint, submitted February 12, https://arxiv.org/abs/1802.03888. Google Scholar
  • Luo X, Andrews M, Fang Z, Phang CW (2013) Mobile targeting. Management Sci. 60(7):1738–1756.LinkGoogle Scholar
  • Luo D, Wang F, Sun J, Markatou M, Hu J, Ebadollahi S (2012) SOR: Scalable orthogonal regression for non-redundant feature selection and its healthcare applications. Ghosh J, Liu H, Davidson I, Domeniconi C, Kamath C, eds. Proc. 2012 SIAM Internat. Conf. Data Mining (SIAM, Philadelphia), 576–587.Google Scholar
  • Marcílio WE, Eler DM (2020) From explanations to feature selection: Assessing SHAP values as feature selection mechanism. 2020 33rd SIBGRAPI Conf. Graphics, Patterns Images (IEEE, Piscataway, NJ), 340–347.Google Scholar
  • Mejia J, Mankad S, Gopal A (2019) A for effort? Using the crowd to identify moral hazard in New York City restaurant hygiene inspections. Inform. Systems Res. 30(4):1363–1386.LinkGoogle Scholar
  • Miyashiro R, Takano Y (2015) Mixed integer second-order cone programming formulations for variable selection in linear regression. Eur. J. Oper. Res. 247(3):721–731.CrossrefGoogle Scholar
  • Mobilewalla (2020) DMP partner. Accessed July 22, 2020, https://www.mobilewalla.com/dmp-partner.Google Scholar
  • Molnar C (2019) Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (Christoph Molnar, Munich, Germany). Google Scholar
  • Murugesan I, Murugesan K, Balasubramanian L, Arumugam M (2019) Interpretation of artificial intelligence algorithms in the prediction of sepsis. 2019 Comput. Cardiology, vol. 46 (IEEE, Piscataway, NJ), 1–4.CrossrefGoogle Scholar
  • Nash JF Jr (1950) The bargaining problem. Econometrica 18(2):155–162. CrossrefGoogle Scholar
  • Nash J (1953) Two person cooperative games. Econometrica 21(1):128–140.CrossrefGoogle Scholar
  • Pang B, Lee L (2008) Opinion mining and sentiment analysis. Foundations Trends Inform. Retrieval 2(1–2):1–135.Google Scholar
  • Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Machine Intelligence 27(8):1226–1238.CrossrefGoogle Scholar
  • Perlich C, Dalessandro B, Hook R, Stitelman O, Raeder T, Provost F (2012) Bid optimizing and inventory scoring in targeted online advertising. Proc. 18th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 804–812.Google Scholar
  • Qazi N, Raza K (2012) Effect of feature selection, smote and under sampling on class imbalance classification. Al-Dabass D, Orsoni A, Cant R, eds. 2012 UKSim 14th Internat. Conf. Comput. Model. Simulation (IEEE, Piscataway, NJ), 145–150.Google Scholar
  • Serrano R (2005) Fifty years of the Nash program 1953–2003. Investigaciones Econom. 29(2):219–258.Google Scholar
  • Shapley LS, Shubik M (1954) A method for evaluating the distribution of power in a committee system. Amer. Political Sci. Rev. 48(3):787–792.CrossrefGoogle Scholar
  • Shmueli G, Koppius OR (2011) Predictive analytics in information systems research. MIS Quart. 35(3):553–572.CrossrefGoogle Scholar
  • Simpli.fi (2021) Simpli.fi’s platform is the solution for media buying organizations. Accessed July 7, 2021, https://simpli.fi/.Google Scholar
  • SmartyAds (2020) Making advertising simple. Accessed July 22, 2020, https://smartyads.com/.Google Scholar
  • SmartyAds (2021) Full stack programmatic advertising at scale. Accessed July 7, 2021, https://smartyads.com/.Google Scholar
  • Sun G, Li J, Dai J, Song Z, Lang F (2018) Feature selection for IoT based on maximal information coefficient. Future Generation Comput. Systems 89(December):606–616.CrossrefGoogle Scholar
  • Sun X, Liu Y, Li J, Zhu J, Chen H, Liu X (2012a) Feature evaluation and selection with cooperative game theory. Pattern Recognition 45(8):2992–3002.CrossrefGoogle Scholar
  • Sun X, Liu Y, Li J, Zhu J, Liu X, Chen H (2012b) Using cooperative game theory to optimize the feature selection problem. Neurocomputing 97(November):86–93.CrossrefGoogle Scholar
  • Swiniarski RW, Skowron A (2003) Rough set methods in feature selection and recognition. Pattern Recognition Lett. 24(6):833–849.CrossrefGoogle Scholar
  • Taneja M, Garg K, Purwar A, Sharma S (2015) Prediction of click frauds in mobile advertising. 2015 Eighth Internat. Conf. Contemporary Comput. (IEEE, Piscataway, NJ), 162–166.Google Scholar
  • Tibshirani R (1996) Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. B 58(1):267–288.CrossrefGoogle Scholar
  • TubeMogul (2020) TubeMogul home page. Accessed July 22, 2020, https://advertising.adobe.com/benefits/.Google Scholar
  • VanderMeer D, Dutta K, Datta A, Ramamritham K, Navanthe SB (2000) Enabling scalable online personalization on the web. Proc. 2nd ACM Conf. Electronic Commerce (ACM, New York), 185–196.Google Scholar
  • Walters RG (1991) Assessing the impact of retail price promotions on product substitution, complementary purchase, and interstore sales displacement. J. Marketing 55(2):17–28.CrossrefGoogle Scholar
  • White GR, Samuel A (2019) Programmatic advertising: Forewarning and avoiding hype-cycle failure. Tech. Forecasting Soc. Change 144(July):157–168.CrossrefGoogle Scholar
  • Xue B, Zhang M, Browne WN, Yao X (2015) A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evolutionary Comput. 20(4):606–626.CrossrefGoogle Scholar
  • Yang Y, Zeng D, Yang Y, Zhang J (2015) Optimal budget allocation across search advertising markets. INFORMS J. Comput. 27(2):285–300.LinkGoogle Scholar
  • Zhang L, Mistry K, Lim CP, Neoh SC (2018) Feature selection using firefly optimization for classification and regression models. Decision Support Systems 106(February):64–85.CrossrefGoogle Scholar
  • Zhang L, Mistry K, Neoh SC, Lim CP (2016) Intelligent facial emotion recognition using moth-firefly optimization. Knowledge-Based Systems 111(November):248–267.CrossrefGoogle Scholar
  • Zhang C, Zhou Y, Guo J, Wang G, Wang X (2019) Research on classification method of high-dimensional class-imbalanced datasets based on SVM. Internat. J. Machine Learn. Cybernetics 10(7):1765–1778.CrossrefGoogle Scholar
  • Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J. Roy. Statist. Soc. B 67(2):301–320.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.