Abstract
This chapter applies the rough set technique to model militarized interstate disputes. One aspect of modeling using rough sets is the issue of granulizing the input data. In this chapter, two granulization techniques are introduced, implemented, and compared. These are the equal-width-bin and equal-frequency-bin partitioning techniques. The rough set model is also compared to the neuro-fuzzy model introduced in Chap. 6. The results obtained demonstrate that equal-width-bin partitioning gives better accuracy than equal-frequency-bin partitioning. However, both techniques were found to give less accurate results than neuro-fuzzy sets. Also, they were found to be more transparent than neuro-rough sets. Furthermore, it is observed that the rules generated from the rough sets are linguistic and easy-to-interpret in comparison with the ones generated from the neuro-fuzzy model.
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References
Araujo, E.: Improved Takagi-Sugeno fuzzy approach. In: IEEE International Conference on Fuzzy Systems, pp. 1154–1158, Hong Kong (2008)
Azadeh, A., Saberi, M., Moghaddam, R.T., Javanmardi, L.: An integrated data envelopment analysis – artificial neural network-rough set algorithm for assessment of personnel efficiency. Expert Syst. Appl. 38, 1364–1373 (2011)
Babuska, R., Verbruggen, H.: Neuro-fuzzy methods for nonlinear system identification. Annu. Rev. Control. 27, 73–85 (2003)
Bazan, J., Nguyen, H.S., Szczuka, M.: A view on rough set concept approximations. Fund. Inform. 59, 107–118 (2004)
Beynon, M.: Reducts within the variable precision rough sets model: a further investigation. Eur. J. Oper. Res. 134, 592–605 (2001)
Bi, Y., Anderson, T., McClean, S.: A rough set model with ontologies for discovering maximal association rules in document collections. Knowl. Based Syst. 16, 243–251 (2003)
Bilski, P.: An unsupervised learning method for comparing the quality of the soft computing algorithms in analog systems diagnostics. Przeglad Elektrotechniczny 86, 242–247 (2010)
Chanas, S., Kuchta, D.: Further remarks on the relation between rough and fuzzy sets. Fuzzy Set. Syst. 47, 391–394 (1992)
Chen, C., Shen, J., Chen, B., Shang, C.-X., Wang. Y.-C.: Building symptoms diagnosis criteria of traditional Chinese medical science treatment on the elderly’s pneumonia by the rough set theory. In: Proceedings of the 29th Chinese Control Conference, pp. 5268–5271, Beijing (2010)
Chen, R., Zhang, Z., Wu, D., Zhang, P., Zhang, X., Wang, Y., Shi, Y.: Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming. J. Theor. Biol. 269, 174–180 (2011)
Coulibaly, P., Evora, N.D.: Comparison of neural network methods for infilling missing daily weather records. J. Hydrol. 341, 27–41 (2007)
Crossingham, B.: Rough set partitioning using computational intelligence approach. MSc thesis, University of the Witwatersrand, Johannesburg (2007)
Crossingham, B., Marwala, T.: Using optimisation techniques to granulise rough set partitions. Comput. Model. Life Sci. 952, 248–257 (2007)
Crossingham, B., Marwala, T.: Using genetic algorithms to optimise rough set partition sizes for HIV data analysis. Stud. Comput. Intell. 78, 245–250 (2008a)
Crossingham, B., Marwala, T.: Using optimisation techniques for discretizing rough set partitions. Int. J. Hybrid Intell. Syst. 5, 219–236 (2008b)
Crossingham, B., Marwala, T., Lagazio, M.: Optimised rough sets for modeling interstate conflict. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 1198–1204, Singapore (2008)
Crossingham, B., Marwala, T., Lagazio, M.: Evolutionarily optimized rough set partitions. ICIC Exp. Lett. 3, 241–246 (2009)
Degang, C., Wenxiu, Z., Yeung, D., Tsang, E.C.C.: Rough approximations on a complete completely distributive lattice with applications to generalized rough sets. Inf. Sci. 176, 1829–1848 (2006)
Deng, T., Chen, Y., Xu, W., Dai, Q.: A novel approach to fuzzy rough sets based on a fuzzy covering. Inf. Sci. 177, 2308–2326 (2007)
Dubois, D.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17, 191–209 (1990)
Fayyad, U., Irani, K.: Multi-interval discretization of continuous valued attributes for classification learning. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1022–1027, Los Alamos (1993)
Goh, C., Law, R.: Incorporating the rough sets theory into travel demand analysis. Tourism Manag. 24, 511–517 (2003)
Gong, J., Yang, H., Zhong, L.: Case-based reasoning based on rough set in rare-earth extraction process. In: Proceedings of the 29th Chinese Control Conference, pp. 70–1706, Beijing (2010)
Grzymala-Busse, J.W., Hu, M.: A comparison of several approaches to missing attribute values in data mining. Lect. Notes Artif. Intell. 205, 378–385 (2001)
Grzymala-Busse, J.W.: Three approaches to missing attribute values – a rough set perspective. In: Proceedings of the IEEE 4th International Conference on Data Mining, pp. 57–64, Brighton (2004)
Grzymala-Busse, J.W., Siddhaye, S.: Rough set approaches to rule induction from incomplete data. In: Proceedings of the 10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, vol. 2, pp. 923–930, Perugia (2004)
Hoa, N.S., Son, N.H.: Rough set approach to approximation of concepts from taxonomy. http://logic.mimuw.edu.pl/publikacje/SonHoaKDO04.pdf (2008)
Huang, C.-C., Liang, W.-Y., Shian-Hua, L., Tseng, T.-L., Chiang, H.-Y.: A rough set based approach to patent development with the consideration of resource allocation. Expert Syst. Appl. 38, 1980–1992 (2011)
Inuiguchi, M., Miyajima, T.: Rough set based rule induction from two decision tables. Eur. J. Oper. Res. 181, 1540–1553 (2007)
Jaafar, A.F.B., Jais, J., Hamid, M.H.B.H.A., Rahman, Z.B.A., Benaouda, D.: Using rough set as a tool for knowledge discovery in DSS. In: Proceedings of the 4th International Conference on Multimedia and Information and Communication Technologies in Education, pp. 1011–1015, Seville, Spain (2006)
Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Toronto (1997)
Jensen, R., Shen, Q.: Semantics-preserving dimensionality reduction: rough and fuzzy-rough based approaches. IEEE Trans. Knowl. Data Eng. 16, 1457–1471 (2004)
Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: A rough set perspective on data and knowledge. In: Klösgen, W., Zytkow, J.M., Klosgen, W., Zyt, J. (eds.) The Handbook of Data Mining and Knowledge Discovery. Oxford University Press, New York (1999)
Kondo, M.: On the structure of generalized rough sets. Inf. Sci. 176, 589–600 (2006)
Leung, Y., Wu, W., Zhang, W.: Knowledge acquisition in incomplete information systems: a rough set approach. Eur. J. Oper. Res. 168, 164–180 (2006)
Liao, S.-H., Chen, Y.-J., Chu, P.-H.: Rough-set-based association rules applied to brand trust evaluation model. Lect. Notes Comp. Sci. 6443, 634–641 (2010)
Lin, C.-S., Tzeng, G.-H., Chin, Y.-C.: Combined rough set theory and flow network graph to predict customer churn in credit card accounts. Expert Syst. Appl. 38, 8–15 (2011)
Liu, S., Chan, F.T.S., Chung, S.H.: A study of distribution center location based on the rough sets and interactive multi-objective fuzzy decision theory. Robot. Comput. Integrated Manuf. 27, 426–433 (2011)
Machowski, L.A., Marwala, T.: Using object oriented calculation process framework and neural networks for classification of image shapes. Int. J. Innov. Comput, Info. Control 1, 609–623 (2005)
Marwala, T.: Computational Intelligence for Missing Data Imputation, Estimation and Management: Knowledge Optimization Techniques. IGI Global Publications, New York (2009)
Marwala, T., Crossingham, B.: Bayesian rough sets. ICIC Exp. Lett. 3, 115–120 (2009)
Marwala, T., Crossingham, B.: Neuro-rough models for modelling HIV. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 3089–3095, Singapore (2008)
Nelwamondo, F.V.: Computational intelligence techniques for missing data imputation. Ph.D. thesis, University of the Witwatersrand, Johannesburg (2008)
Ohrn, A.: Discernibility and rough sets in medicine: tools and applications. Unpublished Ph.D. thesis, Norwegian University of Science and Technology, Trondheim (1999)
Ohrn, A., Rowland, T.: Rough sets: a knowledge discovery technique for multifactorial medical outcomes. Am. J. Phys. Med. Rehabil. 79, 100–108 (2000)
Pawlak, Z.: Rough Sets – Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)
Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning. Inf. Sci. 177, 41–73 (2007)
Pawlak, Z., Wong, S.K.M., Ziarko, W.: Rough sets: probabilistic versus deterministic approach. Int. J. Man. Mach. Stud. 29, 81–95 (1988)
Pawlak, Z., Munakata, T.: Rough control application of rough set theory to control. In: Proceedings of the 4th European Congress on Intelligent Techniques and Soft Computing, pp. 209–218, Aachen, Germany (1996)
Quafafou, M.: α-RST: a generalization of rough set theory. Inf. Sci. 124, 301–316 (2000)
Rowland, T., Ohno-Machado, L., Ohrn, A.: Comparison of multiple prediction models for ambulation following spinal cord injury. In Chute 31, 528–532 (1998)
Salamó, M., López-Sánchez, M.: Rough set based approaches to feature selection for case-based reasoning classifiers. Pattern Recogn. Lett. 32, 280–292 (2011)
Shan, N., Ziarko, W.: Data-based acquisition and incremental modification of classification rules. Comput. Intell. 11, 357–370 (1995)
Slezak, D., Ziarko, W.: The investigation of the bayesian rough set model. Int. J. Approx Reason. 40, 81–91 (2005)
Stefanowski, J.: On rough set based approaches to induction of decision rules. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications. Physica-Verlag, Heidelberg (1998)
Terlecki, P., Walczak, K.: On the relation between rough set reducts and jumping emerging patterns. Inf. Sci. 177, 74–83 (2007)
Tettey, T., Nelwamondo, F.V., Marwala, T.: HIV Data analysis via rule extraction using rough sets. In: Proceedings of the 11th IEEE International Conference on Intelligent Engineering Systems, pp. 105–110, Budapest (2007)
Wang, W., Yang, J., Jensen, R., Liu, X.: Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma. Comput. Meth. Prog. Bio. 83, 147–156 (2006)
Wang, J., Guo, K., Wang, S.: Rough set and tabu search based feature selection for credit scoring. Procedia Comput. Sci. 1, 2433–2440 (2010)
Witlox, F., Tindemans, H.: The application of rough sets analysis in activity based modelling: opportunities and constraints. Expert Syst. Appl. 27, 585–592 (2004)
Wright, S., Marwala, T.: Artificial intelligence techniques for steam generator modelling. arXiv:0811.1711 (2006)
Wu, W., Mi, J., Zhang, W.: Generalized fuzzy rough sets. Inf. Sci. 151, 263–282 (2003)
Xie, F., Lin, Y., Ren, W.: Optimizing model for land use/land cover retrieval from remote sensing imagery based on variable precision rough sets. Ecol. Model. 222, 232–240 (2011)
Yan, W., Liu, W., Cheng, Z., Kan, J.: The prediction of soil moisture based on rough set-neural network model. In: Proceedings of the 29th Chinese Control Conference, pp. 2413–2415, Beijing (2010)
Yang, Y., John, R.: Roughness bound in set-oriented rough set operations. In: Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 1461–1468, Vancouver (2006)
Yao, J.T., Yao, Y.Y.: Induction of classification rules by granular computing. In: Proceedings of the Third International Conference on Rough Sets and Current Trends in Comput, pp. 331–338, Malvern (2002)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Zhang, L., Shao, C.: Designing fuzzy inference system based on improved gradient descent method. J. Syst. Eng. Electron. 17, 853–857 (2006)
Zhang, Y., Zhu, J., Zhang, Z-Y.: The research of reagent adding control in anionic reverse flotation process based on rough set theory. In: Proceedings of the 29th Chinese Control Conference, pp. 3487–3491, Beijing (2010)
Zhao, Y., Yao, Y., Luo, F.: Data analysis based on discernibility and indiscernibility. Inf. Sci. 177, 4959–4976 (2007)
Ziarko, W.: Rough sets as a methodology for data mining. In: Polkowski, L. (ed.) Rough Sets in Knowledge Discovery 1: Methodology and Applications. Physica-Verlag, Heidelberg (1998)
Zou, Z., Tseng, T.-L., Sohn, H., Song, G., Gutierrez, R.: A rough set based approach to distributor selection in supply chain management. Expert Syst. Appl. 38, 106–115 (2011)
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Marwala, T., Lagazio, M. (2011). Rough Sets for Modeling Interstate Conflict. In: Militarized Conflict Modeling Using Computational Intelligence. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-0-85729-790-7_7
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