zbMATH — the first resource for mathematics

Developing an online general type-2 fuzzy classifier using evolving type-1 rules. (English) Zbl 07174612
Summary: General type-2 fuzzy systems have been shown to handle more levels of uncertainty present in the majority of real-world applications. Nevertheless, the rapid growth of information generation does not allow utilizing general type-2 models for their complex learning process. This paper introduces a novel online general type-2 fuzzy classifier (called oGT2FC). It aims to reduce the computations needed to get the type-2 fuzzy sets. As in most online and evolving fuzzy schemes, the initial rule-base in oGT2FC is empty, and then the fuzzy rules are generated in completely online manner; without storing the samples. To specify the type-2 fuzzy sets, oGT2FC employs some experts’ opinions, drawn from training data, to generate automatically some diverse type-1 fuzzy rule-bases. These type-1 rule-bases are updated/evolved by incoming new samples and are used to construct the general type-2 model. By defining a type-2 fuzzy set as the union of vertical slices, oGT2FC performs the type reduction in a fast and efficient manner. The efficiency of the proposed oGT2FC is assessed experimentally, using synthetic and real-world data streams, via comparing with other type-2 and type-1 evolving fuzzy classifiers as well as some state-of-the-art incremental algorithms. In addition, oGT2FC is compared against some fuzzy classifiers in its ability to model uncertainty.
68T37 Reasoning under uncertainty in the context of artificial intelligence
MOA; Pegasos
Full Text: DOI
[1] Gama, J., Knowledge Discovery from Data Streams (2011), Chapman and Hall/CRC: Chapman and Hall/CRC London · Zbl 1230.68017
[2] Widmer, G.; Kubat, M., Learning in the presence of concept drift and hidden contexts, Mach. Learn., 23, 1, 69-101 (1996)
[3] Angelov, P., Autonomous Learning Systems from Data Streams to Knowledge in Real Time (2012), John Wiley and Sons: John Wiley and Sons West Sussex, United Kingdom
[4] Hagras, H.; Doctor, F.; Callaghan, V.; Lopez, A., An incremental adaptive life long learning approach for type-2 fuzzy embedded agents in ambient intelligent environments, IEEE Trans. Fuzzy Syst., 15, 1, 41-55 (2007)
[5] Shahparast, H.; Hamzeloo, S.; Zolghadri Jahromi, M., A self-tuning fuzzy rule-based classifier for data streams, Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 22, 2, 293-304 (2014)
[6] Karnik, N. N.; Mendel, J. M.; Liang, Q., Type-2 fuzzy logic systems, IEEE Trans. Fuzzy Syst., 7, 6, 643-658 (1999)
[7] Zadeh, L. A., The concept of a linguistic variable and its application to approximate reasoning, Inf. Sci., 8, 3, 199-249 (1975) · Zbl 0397.68071
[8] Mendel, J. M.; Bob John, R. I., Type-2 fuzzy sets made simple, IEEE Trans. Fuzzy Syst., 10, 2, 117-127 (2002)
[9] Bouchachia, A.; Vanaret, C., GT2FC: an online growing interval type-2 self-learning fuzzy classifier, IEEE Trans. Fuzzy Syst., 22, 4, 999-1018 (2014)
[10] Pratama, M.; Lu, J.; Zhang, G., Evolving type-2 fuzzy classifier, IEEE Trans. Fuzzy Syst., 24, 3, 574-589 (2015)
[11] Pratama, M.; Lu, J.; Lughofer, E.; Zhang, G.; Anavatti, S., Scaffolding type-2 classifier for incremental learning under concept drifts, Neurocomputing, 191, C, 304-329 (2016)
[12] Lucas, L. A.; Centeno, T. M.; Delgado, M. R., Land cover classification based on general type-2 fuzzy classifiers, Int. J. Fuzzy Syst., 10, 3, 207-216 (2008)
[13] Mizumoto, M.; Tanaka, K., Some properties of fuzzy sets of type 2, Inf. Control, 31, 4, 312-340 (1976) · Zbl 0331.02042
[14] Mendel, J. M.; Liu, F.; Zhai, D., \(α\)-plane representation for type-2 fuzzy sets: theory and applications, IEEE Trans. Fuzzy Syst., 17, 5, 1189-1207 (2009)
[15] Lucas, L. A.; Centeno, T. M.; Delgado, M. R., General type-2 fuzzy inference systems: analysis, design and computational aspects, (Proc. of IEEE International Conference on Fuzzy Systems. Proc. of IEEE International Conference on Fuzzy Systems, London, UK (2007))
[16] Shahparast, H.; Mansoori, E. G.; Zolghadri Jahromi, M., AFCGD: an adaptive fuzzy classifier based on gradient descent, Soft Comput. (2018)
[17] Shahparast, H.; Mansoori, E. G., An online fuzzy model for classification of data streams with drift, (Artificial Intelligence and Signal Processing Conference. Artificial Intelligence and Signal Processing Conference, AISP (2017), Shiraz: Shiraz Iran), 91-95
[18] Bouchachia, A.; Mittermeir, R., Towards incremental fuzzy classifiers, Soft Comput., 11, 2, 193-207 (2007)
[19] Tan, W. W.; Foo, C. L.; Chua, T. W., Type-2 fuzzy system for ECG arrhythmic classification, (IEEE International Fuzzy Systems Conference. IEEE International Fuzzy Systems Conference, London, UK (2007))
[20] Bifet, A.; Holmes, G.; Kirkby, R.; Pfahringer, B., MOA: massive online analysis, J. Mach. Learn. Res., 99, 1601-1604 (2010)
[21] Harries, M., Splice-2 Comparative Evaluation: Electricity Pricing (1999), The University of South: The University of South Wales, Technical Report
[22] Zhang, K.; Fan, W.; Yuan, X.; Davidson, I.; Li, X., Forecasting skewed biased stochastic ozone days: analyses and solutions, (ICDM ’06 Proceedings of the Sixth International Conference on Data Mining (2006))
[23] Katakis, I.; Tsoumakas, G.; Banos, E.; Bassiliades, N.; Vlahavas, I., An adaptive personalized news dissemination system, J. Intell. Inf. Syst., 32, 2, 191-212 (2009)
[24] Zliobaite, I.; Bifet, A.; Holmes, G.; Pfahringer, B., MOA concept drift active learning strategies for streaming data, (Proc. 2nd Workshop Appl. Pattern Anal (2011))
[25] Hulten, G.; Spencer, L.; Domingos, P., Mining time-changing data streams, (Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2001, San Francisco, CA (2001))
[26] Gama, J.; Medas, P.; Castillo, G.; Rodrigues, P., Learning with drift detection, (SBIA Brazilian Symposium on Artificial Intelligence (2004)) · Zbl 1105.68376
[27] Shalev-Shwartz, S.; Singer, Y.; Srebro, N.; Cotter, A., Pegasos: primal estimated sub-gradient solver for SVM, Math. Program., 127, 1, 3-30 (2011) · Zbl 1211.90239
[28] Wang, H.; Fan, W.; Yu, P. S.; Han, J., Mining concept-drifting data streams using ensemble classifiers, (9th ACM International Conference on Knowledge Discovery and Data Mining. 9th ACM International Conference on Knowledge Discovery and Data Mining, SIGKDD, Washington DC, USA (2003))
[29] Pelossof, R.; Jones, M.; Vovsha, I.; Rudin, C., Online coordinate boosting, (2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops) (2010), Kyoto: Kyoto Japan)
[30] Street, N.; Kim, Y., A streaming ensemble algorithm SEA for largescale classification, (Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2001))
[31] Minku, L. L.; White, A. P.; Yao, X., The impact of diversity on online ensemble learning in the presence concept of drift, IEEE Trans. Knowl. Data Eng., 22, 5, 730-742 (May 2010)
[32] Elwell, R.; Polikar, R., Incremental learning of concept drift in nonstationary environments, IEEE Trans. Neural Netw., 22, 10, 1517-1531 (2011)
[33] Minku, L. L.; Yao, X., DDD: a new ensemble approach for dealing with drifts, IEEE Trans. Knowl. Data Eng., 24, 4, 619-633 (Apr. 2012)
[34] Pratama, M.; Anavatti, S. G.; Joo, M.; Lughofer, E., pClass: an effective classifier for streaming examples, IEEE Trans. Fuzzy Syst., 23, 2, 369-386 (2015)
[35] Subramanian, K.; Suresh, S.; Sundararajan, N., A meta-cognitive neuro-fuzzy inference system (McFIS) for sequential classification systems, IEEE Trans. Fuzzy Syst., 21, 6, 1080-1095 (2013)
[36] Pratama, M.; Er, M. J.; Anavatti, S. G.; Lughofer, E.; Wang, N.; Arifin, I., A novel meta-cognitive-based scaffolding classifier to sequential non-stationary classification problems, (IEEE International Conference on Fuzzy Systems. IEEE International Conference on Fuzzy Systems, FUZZ-IEEE, Beijing, China (2014))
[37] Pratama, M.; Anavatti, S. G.; Lu, J., Recurrent classifier based on an incremental meta-cognitive scaffolding algorithm, IEEE Trans. Fuzzy Syst., 23, 6, 2048-2066 (2015)
[38] Demsar, J., Statistical comparisons of classifiers over multiple datasets, J. Mach. Learn. Res., 7, 1-30 (2006) · Zbl 1222.68184
[39] Ikonomovska, E.; Gama, J.; Džeroski, S., Learning model trees from evolving data streams, Data Min. Knowl. Discov., 23, 1, 128-168 (2011) · Zbl 1235.68158
[40] Pratama, M.; Pedrycz, W.; Lughofer, E., Evolving ensemble fuzzy classifier, IEEE Trans. Fuzzy Syst., 26, 5, 2552-2567 (2018)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.