Hierarchical Concept Learning by Fuzzy Semantic Cells
Source abstract
Abstract
Concept modeling and learning have been important research topics in artificial intelligence and knowledge discovery. This paper studies a hierarchical concept learning method that requires a small amount of data to achieve competitive performances. The method starts from a set of fuzzy prototypes called Fuzzy Semantic Cells (FSCs). As a result of FSC parameter optimization, it creates a hierarchical structure of data–prototype–concept. Experiments are conducted to demonstrate the effectiveness of our approach in a classification problem. In particular, when faced with limited training data, our proposed method is comparable with traditional techniques in terms of robustness and generalization ability.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image Retrieval and Classification Techniques · Rough Sets and Fuzzy Logic · Fuzzy Logic and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science, Zhejiang University, Hangzhou 310007, China
College of Industrial Design, Hubei University of Technology, Wuhan 430068, China
References (51)
Show source-provided references
1. Shanks, D.R. (2001). Concept learning and representation. Handbook of Categorization in Cognitive Science, Elsevier. [2nd ed.].
2. On concept algebra: A denotational mathematical structure for knowledge and software modeling10.4018/jcini.2008040101 ↗
3. Interpreting Concept Learning in Cognitive Informatics and Granular Computing10.1109/TSMCB.2009.2013334 ↗
4. Concept learning via granular computing: A cognitive viewpoint10.1016/j.ins.2014.12.010 ↗
5. Natural categories10.1016/0010-0285(73)90017-0 ↗
6. Cognitive representations of semantic categories10.1037/0096-3445.104.3.192 ↗
7. Information and control
8. Probability measures of fuzzy events10.1016/0022-247X(68)90078-4 ↗
9. Goodman, I.R., and Nguyen, H.T. (1991). Uncertainty Models for Knowledge-Based Systems, Naval Ocean Systems Center. Technical Report.
10. Lawry, J., and Tang, Y. (2008). Relating prototype theory and label semantics. Soft Methods for Handling Variability and Imprecision, Springer.10.1007/978-3-540-85027-4_5 ↗
11. Uncertainty modelling for vague concepts: A prototype theory approach10.1016/j.artint.2009.07.006 ↗
12. Linguistic modelling and information coarsening based on prototype theory and label semantics10.1016/j.ijar.2009.01.004 ↗
13. Information cells and information cell mixture models for concept modelling10.1007/s10479-011-1040-y ↗
14. A bipolar model of vague concepts based on random set and prototype theory10.1016/j.ijar.2012.04.003 ↗
15. Learning fuzzy semantic cell by principles of maximum coverage, maximum specificity, and maximum fuzzy entropy of vague concept10.1016/j.knosys.2017.05.014 ↗
16. Semi-supervised cross-entropy clustering with information bottleneck constraint10.1016/j.ins.2017.07.016 ↗
17. Kingma, D., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv.
18. Zhang, H. (2004, January 12–14). The optimality of naive Bayes. Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, Miami Beach, FL, USA.
19. An introduction to kernel and nearest-neighbor nonparametric regression10.1080/00031305.1992.10475879 ↗
20. Quinlan, J.R. (2014). C4. 5: Programs for Machine Learning, Elsevier.
21. Vapnik, V. (2013). The Nature of Statistical Learning Theory, Springer Science & Business Media.
22. A short introduction to boosting
23. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, Curran Associates, Inc.
24. Learning hierarchical concepts based on higher-order fuzzy semantic cell models through the feed-upward mechanism and the self-organizing strategy10.1016/j.knosys.2020.105506 ↗
25. Hierarchical conceptual spaces for concept combination10.1016/j.artint.2016.04.008 ↗
26. Interactive evolutionary optimization of fuzzy cognitive maps10.1016/j.neucom.2016.10.068 ↗
27. Dynamic optimization of fuzzy cognitive maps for time series forecasting10.1016/j.knosys.2016.04.023 ↗
28. Prototype Classification: Insights from Machine Learning10.1162/neco.2009.01-07-443 ↗
29. DrEng, S.A. (2001). Pattern Classification, Springer.
30. Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces10.1109/TPAMI.2003.1195996 ↗
31. Hecht, T., and Gepperth, A.R.T. (2016, January 6–9). Computational advantages of deep prototype-based learning. Proceedings of the International Conference on Artificial Neural Networks (ICANN), Barcelona, Spain.10.1007/978-3-319-44781-0_15 ↗
32. Kasnesis, P., Heartfield, R., Toumanidis, L., Liang, X., Loukas, G., and Patrikakis, C.Z. (2020, January 6–10). A prototype deep learning paraphrase identification service for discovering information cascades in social networks. Proceedings of the 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), London, UK.10.1109/ICMEW46912.2020.9106044 ↗
33. Discriminant Analysis by Gaussian Mixtures10.1111/j.2517-6161.1996.tb02073.x ↗
34. Model-Based Clustering, Discriminant Analysis, and Density Estimation10.1198/016214502760047131 ↗
35. Human-level concept learning through probabilistic program induction10.1126/science.aab3050 ↗
36. Three-way cognitive concept learning via multi-granularity10.1016/j.ins.2016.04.051 ↗
37. The using of data augmentation in machine learning in image processing tasks in the face of data scarcity10.1088/1742-6596/1661/1/012018 ↗
38. Iwana, B.K., and Uchida, S. (2021). An empirical survey of data augmentation for time series classification with neural networks. PLoS ONE, 16.10.1371/journal.pone.0254841 ↗
39. Del Bimbo, A., Cucchiara, R., Sclaroff, S., Farinella, G.M., Mei, T., Bertini, M., Escalante, H.J., and Vezzani, R. (2021). The study of improving the accuracy of convolutional neural networks in face recognition tasks. Pattern Recognition. ICPR International Workshops and Challenges, Springer International Publishing.10.1007/978-3-030-68821-9 ↗
40. Andriyanov, N. (2021). Methods for Preventing Visual Attacks in Convolutional Neural Networks Based on Data Discard and Dimensionality Reduction. Appl. Sci., 11.10.3390/app11115235 ↗
41. Clustering by fast search and find of density peaks10.1126/science.1242072 ↗
42. Clustering by Passing Messages Between Data Points10.1126/science.1136800 ↗
43. Arai, K., Kapoor, S., and Bhatia, R. (2021). Detailed clustering based on gaussian mixture models. Intelligent Systems and Applications, Springer International Publishing.
44. Learning disjunctive concepts based on fuzzy semantic cell models through principles of justifiable granularity and maximum fuzzy entropy10.1016/j.knosys.2018.07.004 ↗
45. Caron, M., Bojanowski, P., Joulin, A., and Douze, M. (2018, January 8–14). Deep clustering for unsupervised learning of visual features. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01264-9_9 ↗
46. Using geographically weighted variables for image classification10.1080/01431161.2011.629637 ↗
47. Dua, D., and Graff, C. (2019). UCI Machine Learning Repository, University of California, School of Information and Computer Science. Available online: http://archive.ics.uci.edu/ml.
48. Gradient-based learning applied to document recognition10.1109/5.726791 ↗
49. Weyrauch, B., Heisele, B., Huang, J., and Blanz, V. (2004, January 2–27). Component-based face recognition with 3D morphable models. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop, Washington, DC, USA.
50. Hinton, G., Srivastava, N., and Swersky, K. (2012). Overview of mini-batch gradient descent. Neural Networks for Machine Learning Lecture, Coursera.
51. Scikit-learn: Machine Learning in Python