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INTERVAL ANALYSIS-BASED HYPERBOX GRANULAR COMPUTING CLASSIFICATION ALGORITHMS | ||
| Iranian Journal of Fuzzy Systems | ||
| مقاله 10، دوره 14، شماره 5، دی 2017، صفحه 139-156 اصل مقاله (213.27 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2017.3437 | ||
| نویسندگان | ||
| Hongbing Liu* 1؛ Jin Li1؛ Huaping Guo2؛ Chunhua Liu2 | ||
| 1Center of Computing, Xinyang Normal University, Xinyang 464000, P. R. China | ||
| 2School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, P. R. China | ||
| چکیده | ||
| Representation of a granule, relation and operation between two granules are mainly researched in granular computing. Hyperbox granular computing classification algorithms (HBGrC) are proposed based on interval analysis. Firstly, a granule is represented as the hyperbox which is the Cartesian product of $N$ intervals for classification in the $N$-dimensional space. Secondly, the relation between two hyperbox granules is measured by the novel positive valuation function induced by the two endpoints of an interval, where the operations between two hyperbox granules are designed so as to include granules with different granularity. Thirdly, hyperbox granular computing classification algorithms are designed on the basis of the operations between two hyperbox granules, the fuzzy inclusion relation between two hyperbox granules, and the granularity threshold. We demonstrate the superior performance of the proposed algorithms compared with the traditional classification algorithms, such as, Random Forest (RF), Support Vector Machines (SVMs), and Multilayer Perceptron (MLP). | ||
| کلیدواژهها | ||
| Fuzzy lattice؛ Granular computing؛ Hyperbox granule؛ Fuzzy inclusion relation | ||
| مراجع | ||
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[1] H. M. Barakat, M. E. El-Adll and A. E. Alyb, Prediction intervals of future observations for a sample of random size from any continuous distribution, Mathematics and Computers in Simulation, 97 (2014), 1-13. [2] G. Bortolan and W. Pedrycz, Hyperbox classifiers for arrhythmia classification, Kybernetes, 36(3-4) (2007), 531-547. [3] Y. Chen, Q. Zhu, K.Wu, S. Zhu and Z. Zeng, A binary granule representation for uncertainty measures in rough set theory, Journal of Intelligent and Fuzzy Systems, 28(2) (2015), 867- 878. [4] C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, 20(3) (1995), 273-297. [5] Z. Fu, K. Ren, J. Shu, X. Sun and F. Huang, Enabling personalized search over encrypted outsourced data with efficiency improvement, IEEE Transactions on Parallel and Distributed Systems, 27(9) (2016), 2546-2559. [6] Z. Fu, X. Sun, N. Linge and L. Zhou, Achieving effective cloud search services: multi-keyword ranked search over encrypted cloud data supporting synonym query, IEEE Transactions on Consumer Electronics, 60(1) (2014), 164-172. [7] T. K. Ho, The random subspace method for constructing decision forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8) (1998), 832-844. [8] P. Honko, Association discovery from relational data via granular computing, Information Sciences, 234 (2013), 136-149. [9] X. Hu, W. Pedrycz and X. Wang, Comparative analysis of logic operators: A perspective of statistical testing and granular computing, International Journal of Approximate Reasoning, 66 (2015), 73-90. [10] V. G. Kaburlasos and A. Kehagias, Fuzzy Inference System (FIS) extensions based on the lattice theory, IEEE Transactions on Fuzzy Systems, 22(3) (2014),531-546. [11] V. G. Kaburlasos and T. P. Pachidis, A Lattice-Computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application, Information Fusion, 16 (2014), 68-83. [12] V. G. Kaburlasos, S. E. Papadakis and G. A. Papakostas, Lattice computing extension of the FAM Neural classifier for human facial expression recognition, IEEE Transactions on Neural Networks and Learning Systems, 24(10) (2013), 1526-1538. [13] V. G. Kaburlasos and G. A. Papakostas, Learning distributions of image features by interactive fuzzy lattice reasoning in pattern recognition applications, IEEE Computational Intelligence Magazine, 10(3) (2015), 42-51. [14] J. Kerr-Wilson and W. Pedrycz, Design of rule-based models through information granulation, Expert System with Applications, 46 (2016),274-285. [15] A. Khalid and I. Beg, Incomplete interval valued fuzzy preference relations, Information Sciences, 348 (2016), 15-24. [16] L. Maciura and J. G. Bazan, Granular computing in mosaicing of images from capsule endoscopy, Natural Computing, 14(4) (2015), 569-577. [17] R. E. Moore, R. B. Kearfott and M. J. Cloud, Introduction to Interval Analysis, SIAM Press, Philadelphia, 2009. [18] K. R. Opara and O. Hryniewicz, Computation of general correlation coefficients for interval data, International Journal of Approximate Reasoning, 73 (2016), 56-75. [19] W. Pedrycz, Granular fuzzy rule-based architectures: Pursuing analysis and design in the framework of granular computing, Intelligent Decision Technologies, 9(4) (2015), 321-330. [20] V. Petridis and V. G. Kaburlasos, Fuzzy lattice neural network (FLNN): a hybrid model for learning, IEEE Transactions on Neural Networks, 9(5) (1998), 877-890. [21] V. Petridis and V. G. Kaburlasos, Learning in the framework of fuzzy lattices, IEEE Transactions on Fuzzy Systems, 7(4) (1999), 422-440. [22] B. Ploj, Advances in Machine Learning Research, Nova Press, New York, 2014. [23] J. Pyrzowski, M. Siemiski, A. Sarnowska, J. Jedrzejczak and W. M. Nyka, Interval analysis of interictal EEG: pathology of the alpha rhythm in focal epilepsy, Scientific Reports 5, 16230 (2015), 1-10. [24] B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge, 1996. [25] M. Ristin, M. Guillaumin, J. Gall and L. V. Gool, Incremental learning of random forests for large-scale image classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3) (2016), 490-503. [26] A. V. Savchenko, Fast multi-class recognition of piecewise regular objects based on sequential three-way decisions and granular computing, Knowledge-Based Systems, 91 (2016), 252-262. [27] H. Sossa and E. Guevara, Efficient training for dendrite morphological neural networks, Neurocomputing, 131 (2014), 132-142. [28] S. Suriadi, C. Ouyang, W. M. P. van der Aalst and A. H. M. ter Hofstede, Event interval analysis: Why do processes take time? Decision Support Systems, 79 (2015), 77-98. [29] Y. Yao and Y. She, Rough set models in multigranulation spaces, Information Sciences, 327 (2016),40-56. [30] Y. Y. Yao and L. Q. Zhao, A measurement theory view on the granularity of partitions, Information Sciences, 213 (2012), 1-13. [31] L. A. Zadeh, Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems, Soft Computing, 2(1) (1998), 23-25. | ||
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