- -

Using negotiable features for prescription problems

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Using negotiable features for prescription problems

Show full item record

Bella Sanjuán, A.; Ferri Ramírez, C.; Hernández Orallo, J.; Ramírez Quintana, MJ. (2011). Using negotiable features for prescription problems. Computing. 91(2):135-168. doi:10.1007/s00607-010-0129-5

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/46833

Files in this item

Item Metadata

Title: Using negotiable features for prescription problems
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Abstract:
Data mining is usually concerned on the construction of accurate models from data, which are usually applied to well-defined problems that can be clearly isolated and formulated independently from other problems. Although ...[+]
Subjects: CRM , Data mining , Function inversion problem , Global optimisation , Monte Carlo method , Negotiable features , Negotiation , Probability estimation , Profit maximisation , Ranking , MONTE CARLO , Estimation , Global optimization , Optimization , Probability , Profitability , Public relations , Monte Carlo methods
Copyrigths: Cerrado
Source:
Computing. (issn: 0010-485X )
DOI: 10.1007/s00607-010-0129-5
Publisher:
Springer Verlag (Germany)
Publisher version: http://dx.doi.org/10.1007/s00607-010-0129-5
Thanks:
This work has been partially supported by the EU (FEDER) and the Spanish MEC/MICINN, under grant TIN 2007-68093-C02, the Spanish project "Agreement Technologies" (Consolider Ingenio CSD2007-00022) and the GVA project ...[+]
Type: Artículo

References

Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6): 734–749

Bella A, Ferri C, Hernández-Orallo J, Ramírez-Quintana MJ (2007) Joint cutoff probabilistic estimation using simulation: a mailing campaign application. In: IDEAL, volume 4881 of LNCS. Springer, New York, pp 609–619

Berry MJA, Linoff GS (1999) Mastering data mining: the art and science of customer relationship management. Wiley, New York [+]
Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6): 734–749

Bella A, Ferri C, Hernández-Orallo J, Ramírez-Quintana MJ (2007) Joint cutoff probabilistic estimation using simulation: a mailing campaign application. In: IDEAL, volume 4881 of LNCS. Springer, New York, pp 609–619

Berry MJA, Linoff GS (1999) Mastering data mining: the art and science of customer relationship management. Wiley, New York

Berson A, Smith S, Thearling K (2000) Building data mining applications for CRM. McGraw Hill

Bertsekas DP (2005) Dynamic programming and optimal control, 3rd edn. Massachusetts Institute of Technology

Better M, Glover F, Laguna M (2007) Advances in analytics: integrating dynamic data mining with simulation optimization. IBM J Res Dev 51(3): 477–487

Bradley N (2007) Marketing research. Tools and techniques. Oxford University Press

Carbo J, Ledezma A (2003) A machine learning based evaluation of a negotiation between agents involving fuzzy counter-offers. In: AWIC, pp 268–277

Devroye L, Györfi L, Lugosi G (1997) A probabilistic theory of pattern recognition. In: Stochastic modelling and applied probability. Springer, New York

Ferri C, Flach PA, Hernández-Orallo J (2003) Improving the AUC of probabilistic estimation trees. In: 14th European conference on machine learning, proceedings. Springer, New York, pp 121–132

Ferri C, Hernández-Orallo J, Modroiu R (2009) An experimental comparison of performance measures for classification. Pattern Recogn Lett 30(1): 27–38

Fudenberg D, Tirole J (1991) Game theory. MIT Press, Cambridge

Gustafsson A, Herrmann A, Huber F (2000) Conjoint analysis as an instrument of market research practice. In: Conjoint measurement. Springer, Berlin, pp 3–30

Han J, Kamber M (2006) Data mining: concepts and techniques. Morgan Kaufmann, San Mateo

Heckman JJ (1979) Sample selection bias as a specification error. Econometrica 47: 153–161

Jennings NR, Faratin P, Lomuscio AR, Parsons S, Wooldridge MJ, Sierra C (2001) Automated negotiation: prospects, methods and challenges. Group Decis Negot 10(2): 199–215

Kilian C (2005) Modern control technology. Thompson Delmar Learning

Li S, Sun B, Wilcox RT (2005) Cross-selling sequentially ordered products: an application to consumer banking services. J Mark Res 42: 233–239

Metropolis N, Ulam S (1949) The Monte Carlo method. J Am Stat Assoc 44: 335–341

Padmanabhan B, Tuzhilin A (2003) On the use of optimization for data mining: theoretical interactions and ecrm opportunities. Manage Sci 49(10, Special Issue on E-Business and Management Science):1327–1343

Peterson M (2009) An introduction to decision theory. Cambridge University Press, Cambridge

Prinzie A, Van den Poel D (2005) Constrained optimization of data-mining problems to improve model performance: a direct-marketing application. Expert Syst Appl 29(3): 630–640

Prinzie A, Van den Poel D (2006) Exploiting randomness for feature selection in multinomial logit: a crm cross-sell application. In: Advances in data mining. Lecture notes in computer science, vol 4065. Springer, Berlin, pp 310–323

Puterman ML (1994) Markov decision processes. Wiley, New York

Quinlan JR (1992) Learning with continuous classes. In: 5th Australian joint conference on artificial intelligence. World Scientific, Singapore, pp 343–348

Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo

Russell SJ, Norvig P (2003) Artificial intelligence: a modern approach. Pearson Education, Upper Saddle River

Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge

Vetsikas I, Jennings N (2010) Bidding strategies for realistic multi-unit sealed-bid auctions. J Auton Agents Multi-Agent Syst 21(2): 265–291

Weisstein EW (2003) CRC concise encyclopedia of mathematics. CRC Press, Boca Raton

Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques with Java implementations. Elsevier, Amsterdam

Wooldridge M (2002) An introduction to multiagent systems. Wiley, New York

Zhang S, Ye S, Makedon F, Ford J (2004) A hybrid negotiation strategy mechanism in an automated negotiation system. In: ACM conference on electronic commerce. ACM, pp 256–257

[-]

This item appears in the following Collection(s)

Show full item record