Combining case-based reasoning systems and support vector regression to evaluate the atmosphere-ocean interaction

Knowledge and Information Systems

Volumn 30, Issue 1, 2012, Pages 155-177

  de Paz, Juan F ^a   Bajo, Javier ^b   González, Angélica ^a   Rodríguez, Sara ^a   Corchado, Juan M ^a  

^a UNIVERSITY OF SALAMANCA   (Spain)

^b UNIVERSIDAD PONTIFICIA DE SALAMANCA   (Spain)

Author keywords

Atmosphere ocean interaction; Case based reasoning; Decision support; Support vector regression

Indexed keywords

EID: 84855530396     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-010-0368-y     Document Type: Article

References (80)

1. Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun 7: 39-59.
2. Aarts E (2004) Ambient intelligence, third international conference, AH 2004. LNCS, pp 1.
3. Aiken J, Fishwick JR, Lavender SJ, Barlow R, Moore GF, Sessions H, Bernard S, Ras J, Hardman-Mountford NJ (2007) Validation of MERIS reflectance and chlorophyll during the BENCAL cruise October, 2002: preliminary validation and new products for phytoplankton functional types and photosynthetic parameters. Int J Remote Sens 28(3-4): 497-516.
4. 2. In: Proceedings of the ENVISAT symposium.
5. Bajo J, Corchado JM (2005) Evaluation and monitoring of the air-sea interaction using a CBR-agents approach. In: Proceedings of the ICCBR 2005 LNCS, vol 3620, pp 50-62.
6. Bajo J, Corchado JM (2005) Multiagent architecture for monitoring the North-Atlantic carbon dioxide exchange rate. In: Proceedings of the CAEPIA 2005 LNCS, vol 4177, pp 321-330.
7. Bajo J, Corchado JM, De Paz Y, De Paz JF, Martín Q (2006) A multiagent recommending system for shopping centres. In: Proceedings of the workshop on recommender systems (ECAI 2006), vol 1, pp 92-106.
8. Baruque B, Corchado E, Mata A, Corchado JM (2010) A forecasting solution to the oil spill problem based on a hybrid intelligent system. Inf Sci 180(10): 2029-2043.
9. Bauer B, Huget MP (2003) FIPA modeling: agent class diagrams.
10. Bellifime F, Poggi A, Rimasa G (2001) JADE: a FIPA2000 compliant agent development environement. In: Proceedings of the 5th international conference on autonomous agents. ACM, pp 216-217.
11. Bergmann R, Wilke W (1996) On the role of abstraction in case-based reasoning. In: Proceedings of the EWCBR-96 European conference on case-based reasoning LNCS, vol 1186, pp 28-43.
12. Bratman ME (1987) Intentions, plans and practical reason. Harvard University Press, Cambridge.
13. Bratman ME, Israel D, Pollack ME (1988) Plans and resource-bounded practical reasoning. Comput Intell 4: 349-355.
14. Corchado JM, Lees B (2001) A hybrid case-based model for forecasting. Appl Artif Intell 15(2): 105-127.
15. Corchado JM, Laza R (2003) Constructing deliberative agents with case- based reasoning technology. Int J Intell Syst 18(12): 1227-1241.
16. Corchado JM, Aiken J, Corchado E, Lefevre N, Smyth T (2004) Quantifying the ocean's CO2 budget with a CoHeL-IBR system. In: Proceedings of the 7th European conference on case-based reasoning. LNCS, vol 3155, pp 533-546.
17. Corchado JM, Pavón J, Corchado E Castillo LF (2005) Development of CBR-BDI agents: a tourist guide application. In: Proceedings of the 7th European conference on case-based reasoning 2004. LNAI, vol 3155, pp 547-559.
18. Chen-Chia C, Zne-Jung L (2011) Hybrid robust support vector machines for regression with outliers. Appl Soft Comput 11(1): 64-72.
19. Chi-Jie L, Tian-Shyug L, Chih-Chou C (2009) Financial time series forecasting using independent component analysis and support vector regression. Decis Support Syst 47(2): 115-125.
20. DeLoach S (2001) Anlysis and design using MaSE and AgentTool. In: Proceedings of the 12th midwest artificial intelligence and cognitive science conference (MAICS).
21. Dransfeld S, Tatnall AR, Robinson IS, Mobley CD (2004) A comparison of multi-layer perceptron and multilinear regression algorithms for the inversion of synthetic ocean colour spectra. Int J Remote Sens 25(21): 4829-4834.
22. Dransfeld S, Tatnall AR, Robinson IS, Mobley CD (2005) Prioritizing ocean colour channels by neural network input reflectance perturbation. Int J Remote Sens 26(5): 1043-1048.
23. EURESCOM MESSAGE (2001) Methodology for engineering systems of software agents. Technical report P907-TI1, EURESCOM.
24. Farquad MAH, Ravi V, Bapi Raju S (2008) Support vector regression based hybrid rule extraction methods for forecasting. Expert Syst Appl 37(8): 5577-5589.
25. Feret MP, Glasgow JI (1994) Experienced-aided diagnosis for complex devices. In: Proceedings of the 12th national conference an artificial intelligence, pp 29-35.
26. FIPA Foundation for Intelligent Physical Agents. http://www. fipa. org. Accessed 20 Mar 2010.
27. Fyfe C, Corchado ES (2002) Maximum likelihood hebbian rules. In: Proceedings of the European symposium on artificial neural networks, pp 143-148. http://www. pml. ac. uk/.
28. Glez-Bedia M, Corchado JM, Corchado ES, Fyfe C (2002) Analytical model for constructing deliberative agents. Eng Intell Syst 3: 173-185.
29. González-Romera E, Jaramillo-Morán MA, Carmona-Fernández D (2008) Monthly electric energy demand forecasting with neural networks and Fourier series. Energy Convers Manag 49(11): 3135-3142.
30. Corchado E, Corchado E, Saiz L, Abraham A (2010) DIPKIP: a connectionist knowledge management system to identify knowledge deficits in practical cases. Comput Intell 26(1): 26-56.
31. Joh DY (1997) CBR in a changing environment. Case-based reasoning research and development. In: Proceedings of the ICCBR-97, p 126.
32. Hussain A, Jaffar MA, MirzaAM (2010) Ahybrid image restoration approach: fuzzy logic and directional weighted median based uniform impulse noise removal. Knowl Inf Syst 24(1): 77-90.
33. Iglesias C, Garijo M, Gonzalez JC, Velasco JR (1998) Analysis and design of multiagent systems using MAS-CommonKADS. Inteligent Agents IV 1365: 313-326.
34. Javeed SSAK, Al-Garni AZ (1995) Forecasting electric energy consumption using neural networks. Energy Policy 23(12): 1097-1104.
35. Kinny D, Georgeff M (1991) Commitment and effectiveness of situated agents. In: Proceedings of the twelfth international joint conference on artificial intelligence, pp 82-88.
36. 2 exchange in the Tropical Atlantic. Ocean Model 19(3-4): 161-182.
37. 2 exchange. Ocean Model 23(1-2): 33-48.
38. Odell J, Huget MP (2003) FIPA modeling: interaction diagrams.
39. Kolodner J (1983) Maintaining organization in a dynamic long-term memory. Cognit Sci 7: 243-280.
40. Kolodner J (1983) Reconstructive memory, a computer model. Cognit Sci 7(4): 281-328.
41. Kolodner J (1993) Case-based reasoning. Morgan Kaufmann, Los Altos.
42. Kolodner J (1993) Maintaining organization in a dynamic long-term memory. Morgan Kaufmann, Los Altos.
43. Lavender SJ, Pinkerton MH, Moore GF, Aiken J, Blondeau-Patissier D (2005) Modification to the atmospheric correction of SeaWIFS ocean colour images over turbid waters. Cont Shelf Res 25: 539-555.
44. 2 in the coastal upwelling off Chile: sapatial and temporal extrapolation using satellite data. J Geophys Res 107(6): 8. 1-8. 15.
45. Leake D, Kendall-Morwick J (2008) Towards case-based support for e-science workflow generation by mining provenance. Adv Case Base Reson 5239: 269-283.
46. Lessmann S, Voß S (2009) A reference model for customer-centric data mining with support vector machines. Eur J Oper Res 199(2): 520-530.
47. Li H, Liang Y, Xu Q (2009) Support vector machines and its applications in chemistry. Chemom Intell Lab Syst 95(2): 188-198.
48. Liu S, Duffy AHB, Whitfield RI, Boyle IM (2010) Integration of decision support systems to improve decision support performance. Knowl Inf Syst 22(3): 261-286.
49. Lo S (2008) Web service quality control based on text mining using support vector machine. Expert Syst Appl 34(1): 603-610.
50. Lu J, Li R, Zhang Y, Zhao T, Lu Z (2010) Image annotation techniques based on feature selection for class-pairs. Knowl Inf Syst 24(2): 325-337.
51. Martín FJ, Plaza E, Arcos JL (1999) Knowledge and experience reuse through communications among competent (peer) agents. Int J Softw Eng Knowl Eng 9(3): 319-341.
52. Melesse AM, Hanley RS (2005) Artificial neural networknext term application for multi-ecosystem carbon flux simulation. Ecol Model 189(3-4): 305-314.
53. Montaño JJ, Palmer A (2002) Artificial neural networks, opening the black box. Metodología de las Ciencias del Comportamiento 4(1): 77-93.
54. Nwana HS, Ndumu DT, Lee LC, Collins JC (1999) ZEUS: a toolkit for building distributed multi-agent systems. Appl Artif Intell J 1(13): 129-185.
55. Nguyen H, Chan W (2004) Multiple neural networks for a long term time series forecast. Neural Comput Appl 13(1): 90-98.
56. Odell J, Levy R, Nodine M (2004) FIPA modeling TC: agent class superstructure metamodel. FIPA meeting and interim work.
57. Olivia C, Chang CF, Enguix CF, Ghose AK (1999) Case-based BDI agents: an effective approach for intelligent search on the world wide web. In: Proceedings of the AAAI spring symposium on intelligent agents, pp 22-24.
58. OMG Unified Modelling Language Specification. Version 1. 3. http://www. omg. org. Accessed 20 Mar 2010.
59. Perner P (1998) Different learning strategies in a case-based reasoning system for image interpretation. Advances in case-based reasoning. LNCS, vol 1488, pp 251-261.
60. O'Reilly JE, Maritorena S, Mitchell BG, Siegel DA, Carder KL, Garver SA, Kahru M, McClain C (1999) Ocean color chlorophyll algorithms for SeaWIFS. J Geophys Res Oceans 103(11): 24937-24953.
61. Osowski S, Garanty K (2007) Forecasting of the daily meteorological pollution using wavelets and support vector machine. Eng Appl Artif Intell 20(6): 745-755.
62. Perner P (2001) Why case-based reasoning is attractive for image interpretation. Case-based reasoning research and development LNCS, vol 2080, pp 27-43.
63. Pokahr A, Braubach L, Lamersdorf W (2003) Jadex: implementing a BDI-infrastructure for JADE agents. In: EXP-in search of innovation (Special Issue on JADE) 3(3): 76-85.
64. Ping-Feng P, Kuo-Ping L, Chi-Shen L, Ping-Teng C (2010) Time series forecasting by a seasonal support vector regression model. Expert Syst Appl 37(6): 4261-4265.
65. Rao AS, Georgeff MP (1995) BDI agents: from theory to practice. In: Proceedings of the first international conference on multi-agent systems, pp 312-319.
66. Seung HS, Socci ND, Lee D (1998) The rectified gaussian distribution. In: Proceedings of the advances in neural information processing systems, vol 10, pp 350-356.
67. 2 transfer due to water-side convection. J Mar Syst 80(1-20): 125-134.
68. Sarmiento JL, Dender M (1994) Carbon biogeochemistry and climate change. Photosynth Res 39: 209-234.
69. The Beam Project (2010) http://www. brockmann-consult. de/beam/. Accessed 20 Mar 2010.
70. Smola A, Scolköpf B (2003) A tutorial on support vector regression. Stat Comput 14: 199-222.
71. 2 and nutrients in the high-latitude surface oceans: a comparative study. Glob biochem Cycles 7(4): 843-878.
72. Wendler J, Lenz M (1998) CBR for dynamic situation assessment in an agent-oriented setting. In: Proceedings of the AAAI-98 workshop on CBR integrations, pp 172-176.
73. Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330(3-4): 621-640.
74. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10: 988-999.
75. Vapnik V (1995) The Nature of statistical learning theory. Springer, Berlin.
76. Wooldridge M, Jennings NR (1994) Agent theories, architectures, and languages: a survey. Intelligent agents. LNCS, pp 1-39.
77. Abbas S, Sawamura H (2008) Argument mining using highly structured argument repertoire. In: The first international conference on educational data mining (EDM), Montreal, Qubec, Canada, pp 202-2010.
78. Wooldridge M, Jennings NR, Kinny D (2000) The gaia methodology for agent-oriented analysis and design. J Auton Agents Multi Agent Syst 3(3): 285-312.
79. Yang Q, Li X, Shi X (2008) Cellular automata for simulating land use changes based on support vector machines. Comput Geosci 34(6): 592-602.
80. Zeichen MM, Robinson IS (2004) Detection and monitoring of algal blooms using SeaWIFS imagery. Int J Remote Sens 25(9): 1797-1798.