{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:45:25Z","timestamp":1760402725467,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,13]],"date-time":"2020-01-13T00:00:00Z","timestamp":1578873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010447","name":"Kementerian Riset, Teknologi dan Pendidikan Tinggi","doi-asserted-by":"publisher","award":["No.521\/UN2.R3.1\/HKP05.00\/2018"],"award-info":[{"award-number":["No.521\/UN2.R3.1\/HKP05.00\/2018"]}],"id":[{"id":"10.13039\/501100010447","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>One of the challenges in machine learning is a classification in multi-modal data. The problem needs a customized method as the data has a feature that spreads in several areas. This study proposed a multi-codebook fuzzy neural network classifiers using clustering and incremental learning approaches to deal with multi-modal data classification. The clustering methods used are K-Means and GMM clustering. Experiment result, on a synthetic dataset, the proposed method achieved the highest performance with 84.76% accuracy. Whereas on the benchmark dataset, the proposed method has the highest performance with 79.94% accuracy. The proposed method has 24.9% and 4.7% improvements in synthetic and benchmark datasets respectively compared to the original version. The proposed classifier has better accuracy compared to a popular neural network with 10% and 4.7% margin in synthetic and benchmark dataset respectively.<\/jats:p>","DOI":"10.3390\/computation8010006","type":"journal-article","created":{"date-parts":[[2020,1,13]],"date-time":"2020-01-13T04:05:51Z","timestamp":1578888351000},"page":"6","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clustering versus Incremental Learning Multi-Codebook Fuzzy Neural Network for Multi-Modal Data Classification"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9251-7781","authenticated-orcid":false,"given":"Muhammad Anwar","family":"Ma\u2019sum","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Universitas Indonesia, Kampus Baru UI Depok, Jawa Barat 16424, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hadaiq Rolis","family":"Sanabila","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Universitas Indonesia, Kampus Baru UI Depok, Jawa Barat 16424, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Petrus","family":"Mursanto","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Universitas Indonesia, Kampus Baru UI Depok, Jawa Barat 16424, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wisnu","family":"Jatmiko","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Universitas Indonesia, Kampus Baru UI Depok, Jawa Barat 16424, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,13]]},"reference":[{"key":"ref_1","first-page":"423","article-title":"Multimodal machine learning: A survey and taxonomy","volume":"41","author":"Ahuja","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.neunet.2014.10.005","article-title":"Towards an intelligent framework for multimodal affective data analysis","volume":"63","author":"Poria","year":"2015","journal-title":"Neural Netw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s00530-010-0182-0","article-title":"Multimodal fusion for multimedia analysis: A survey","volume":"16","author":"Atrey","year":"2010","journal-title":"Multimed. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1109\/TPAMI.2016.2515606","article-title":"Survey on rgb, 3d, thermal, and multimodal approaches for facial expression recognition: History, trends, and affect-related applications","volume":"38","author":"Corneanu","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.imavis.2017.08.003","article-title":"A survey of multimodal sentiment analysis","volume":"65","author":"Soleymani","year":"2017","journal-title":"Image Vis. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1007\/s10278-013-9619-2","article-title":"Content-based medical image retrieval: A survey of applications to multidimensional and multimodality data","volume":"26","author":"Kumar","year":"2013","journal-title":"J. Digit. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s10462-012-9332-4","article-title":"Multimodal feature extraction and fusion for semantic mining of soccer video: A survey","volume":"42","author":"Oskouie","year":"2014","journal-title":"Artif. Intell. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1145\/1456650.1456657","article-title":"Survey and analysis of multimodal sensor planning and integration for wide area surveillance","volume":"41","author":"Abidi","year":"2009","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kiela, D., Grave, E., Joulin, A., and Mikolov, T. (2018, January 2\u20137). Efficient large-scale multi-modal classification. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11945"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Vortmann, L.M., Schult, M., Benedek, M., Walcher, S., and Putze, F. (2019, January 14\u201318). Real-Time Multimodal Classification of Internal and External Attention. Proceedings of the Adjunct of the 2019 International Conference on Multimodal Interaction, Suzhou, China.","DOI":"10.1145\/3351529.3360658"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.neuroimage.2011.01.008","article-title":"Multimodal classification of Alzheimer\u2019s disease and mild cognitive impairment","volume":"55","author":"Zhang","year":"2011","journal-title":"Neuroimage"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Molina, J.F.G., Zheng, L., Sertdemir, M., Dinter, D.J., Sch\u00f6nberg, S., and R\u00e4dle, M. (2014). Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0093600"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1650025","DOI":"10.1142\/S0129065716500258","article-title":"Ensembles of deep learning architectures for the early diagnosis of the Alzheimer\u2019s disease","volume":"26","author":"Ortiz","year":"2016","journal-title":"Int. J. Neural Syst."},{"key":"ref_14","unstructured":"Ma\u2019sum, M.A., Sanabila, H., and Jatmiko, W. (2015, January 10\u201311). Multi codebook LVQ-based artificial neural network using clustering approach. Proceedings of the 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Depok, Indonesia."},{"key":"ref_15","first-page":"100","article-title":"Algorithm AS 136: A k-means clustering algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"J. R. Stat. Society. Ser. C Applied Stat."},{"key":"ref_16","unstructured":"McLachlan, G.J., and Basford, K.E. (1988). Mixture Models: Inference and Applications to Clustering, Marcel Dekker."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mirkin, B. (2005). Clustering for Data Mining: A Data Recovery Approach, Chapman and Hall\/CRC.","DOI":"10.1201\/9781420034912"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1109\/72.80266","article-title":"The multilayer perceptron as an approximation to a Bayes optimal discriminant function","volume":"1","author":"Ruck","year":"1990","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_20","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1109\/TITB.2009.2037317","article-title":"SVM-based multimodal classification of activities of daily living in health smart homes: Sensors, algorithms, and first experimental results","volume":"14","author":"Fleury","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1109\/JPROC.2015.2449668","article-title":"Multimodal classification of remote sensing images: A review and future directions","volume":"103","author":"Tuia","year":"2015","journal-title":"Proc. IEEE"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gallo, I., Calefati, A., and Nawaz, S. (2017, January 9\u201315). Multimodal Classification Fusion in Real-World Scenarios. Proceedings of the 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan.","DOI":"10.1109\/ICDAR.2017.326"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1109\/TNSRE.2018.2813138","article-title":"A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series","volume":"26","author":"Chambon","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kohonen, T. (1990, January 17\u201321). Improved versions of learning vector quantization. Proceedings of the 1990 IJCNN International Joint Conference on Neural Networks, San Diego, CA, USA.","DOI":"10.1109\/IJCNN.1990.137622"},{"key":"ref_27","unstructured":"Sato, A., and Yamada, K. (1996). Generalized learning vector quantization. Advances in Neural Information Processing Systems, The MIT Press."},{"key":"ref_28","unstructured":"Setiawan, I.M.A., Imah, E.M., and Jatmiko, W. (2011, January 17\u201318). Arrhytmia classification using fuzzy-neuro generalized learning vector quantization. Proceedings of the 2011 International Conference on Advanced Computer Science and Information System (ICACSIS), Jakarta, Indonesia."},{"key":"ref_29","unstructured":"Rachmadi, M.F., Ma\u2019sum, M.A., Setiawan, I.M.A., and Jatmiko, W. (2012, January 20\u201323). Fuzzy learning vector quantization particle swarm optimization (FLVQ-PSO) and fuzzy neuro generalized learning vector quantization (FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram. Proceedings of the 2012 SICE Annual Conference (SICE), Akita, Japan."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.cogsys.2016.08.002","article-title":"Emergence of multimodal action representations from neural network self-organization","volume":"43","author":"Parisi","year":"2017","journal-title":"Cogn. Syst. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5697","DOI":"10.1038\/s41598-018-22871-z","article-title":"Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer\u2019s Disease using structural MR and FDG-PET images","volume":"8","author":"Lu","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/S0019-0578(07)60097-4","article-title":"Fuzzy-neuro LVQ and its comparison with fuzzy algorithm LVQ in artificial odor discrimination system","volume":"41","author":"Kusumoputro","year":"2002","journal-title":"ISA Trans."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/8\/1\/6\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:05:01Z","timestamp":1760364301000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/8\/1\/6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,13]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["computation8010006"],"URL":"https:\/\/doi.org\/10.3390\/computation8010006","relation":{},"ISSN":["2079-3197"],"issn-type":[{"type":"electronic","value":"2079-3197"}],"subject":[],"published":{"date-parts":[[2020,1,13]]}}}