Weighted pretopological approach for decision accuracy in information system
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Keywords:
Pretopology, Information system, Decision accuracyAbstract
Computing decision accuracy is an important step in making and choosing decision in information system. Most works in this direction does not use the concepts of topology. This work is to use pretopological structures generated from weighted similarity classes to find accuracy of decision sets. Example is given to indicate the approach and comparison between weighted accuracy and other types of accuracy
References
Pawlak, Z., and Skowron, A., Rough membership functions in advances in the DempsterâĂŘShafer theory of evidence, John Wiley & Sons, Inc, 22, (1994).
Frelicot, C., et al., In: A Pretopological Approach for Pattern Recognition with Reject Options, Lecture Notes in Computer Science, 1451, (1998), 707–715.
Vincent, D., Brissaud, M., and Lamure, M., International Journal of Pure and Applied Mathematics, 15, (2009), 391–402.
Kamel EL-Sayed, M., Topological Based Measures for Decisions in Information Systems, International Conference on Mathematics and Engineering, 10–12 May, Istanbul, Turkey, (2017).
Kamel EL-Sayed, M., Attribute Reduction Based on Covering Granules and Boolean Reasoning, Journal of Computational and Theoretical Nanoscience, American Scientific Publishers, 15, (2018), 1072–1075.
Largeron, C., and Bonnevay, S., A pretopological approach for structural analysis, Inf. Sci., 144 (1–4), (2002).
Kryszkiewicz, M., Rough set approach to incomplete information systems, Information Sciences, 112 (1–4), (1998), 39–49.
Kamel EL-Sayed, M., Similarity Based Membership of Elements to Uncertain Concept in Information System, 20th International Conference on Mathematical and Statistical Sciences, France, Paris, March 15–16, 20 (3), (2018).
Hall, M., and Holmes, G., Benchmarking Attribute Selection Techniques for Discrete Class Data Mining, IEEE Transactions on Knowledge and Data Engineering, 15, (2003), 1437–1447.
Herawan, T., and Meseri, W., Rough set membership function-based for clustering web transactions, International Journal of Multimedia and Ubiquitous engineering, 8, (2013), 105–118.
Al-shami, T. M., and El-Shafei, M. E., T-soft equality relation, Turkish Journal of Mathematics, 44 (4), (2020),1427–1441.
Elshenawy, A., Kamel EL-Sayed, M., and Elsodany, E., Weighted membership based on matrix relation, Mathematical Methods in the Applied Sciences, John Wiley & Sons, Inc., 32, (2017), 1–9.
A l-shami, T., Topological approach to generate new rough set models, Complex & Intelligent Systems, 8, 4101–4113, 2022.
Nasr, A., El Ghawalby, H., and Mareay, R., Pretopological applications for attribute reduction in information systems, Alfarama Journal of Basic & Applied Sciences, 3, (2022).
Meziane, A., et al., Satellite image segmentation by mathematical pretopology and automatic classification, Image processing, signal processing and synthetic aperture radar for remote sensing, London (England), SPIE Proc., 31, (1997), 232–236.
Liu, Y., and Schumann, M., Data mining feature selection for credit scoring models, Journal of the Operational Research Society, 56, (2005), 1099–1108.
Changzhong, W., Baiqing, S., and Qinhua, H., An Improved Approach to Attribute Reduction with Covering Rough Sets, Cornell University Library, 32, (2012), 1–15.
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