{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:00:09Z","timestamp":1764842409756,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T00:00:00Z","timestamp":1596499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of the data whilst avoiding the effect of the outdated stored knowledge in neural networks (NN). This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). The framework trains the NN on previously labeled data and its knowledge is used to calculate the error of the core online-offline clustering block. The genetic optimization is responsible for selecting the best parameters of the core model to minimize the error. This integration aims to handle the concept drift. We designated this model as hyper-heuristic framework for semi-supervised classification or HH-F. Experimental results of the application of HH-F on real datasets prove the superiority of the proposed framework over the existing state-of-the art approaches used in the literature for sequential classification data with evolving nature.<\/jats:p>","DOI":"10.3390\/sym12081292","type":"journal-article","created":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T05:56:46Z","timestamp":1596520606000},"page":"1292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Hyper-Heuristic Framework for Sequential Semi-Supervised Classification Based on Core Clustering"],"prefix":"10.3390","volume":"12","author":[{"given":"Ahmed","family":"Adnan","sequence":"first","affiliation":[{"name":"Department of Communication Technology and Networks, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang 43300, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0088-7073","authenticated-orcid":false,"given":"Abdullah","family":"Muhammed","sequence":"additional","affiliation":[{"name":"Department of Communication Technology and Networks, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang 43300, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdul Azim","family":"Abd Ghani","sequence":"additional","affiliation":[{"name":"Department of Software Engineering and Information System, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang 43300, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azizol","family":"Abdullah","sequence":"additional","affiliation":[{"name":"Department of Communication Technology and Networks, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang 43300, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fahrul","family":"Hakim","sequence":"additional","affiliation":[{"name":"Department of Communication Technology and Networks, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang 43300, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A.V., and Rong, X. (2015). Data mining for the internet of things: Literature review and challenges. Int. J. Distrib. Sens. Netw., 2015.","DOI":"10.1155\/2015\/431047"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.is.2014.07.006","article-title":"The rise of \u201cbig data\u201d on cloud computing: Review and open research issues","volume":"47","author":"Abaker","year":"2015","journal-title":"Inf. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.inffus.2015.08.005","article-title":"Social big data: Recent achievements and new challenges","volume":"28","author":"Jung","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.procir.2015.08.026","article-title":"Industrial big data analytics and cyber-physical systems for future maintenance & service innovation","volume":"38","author":"Lee","year":"2015","journal-title":"Procedia CIRP"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Moustafa, N., Creech, G., and Slay, J. (2017). Big data analytics for intrusion detection system: Statistical decision-making using finite dirichlet mixture models. Data Analytics and Decision Support for Cybersecurity, Springer.","DOI":"10.1007\/978-3-319-59439-2_5"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1007\/s11036-016-0745-1","article-title":"Smart clothing: Connecting human with clouds and big data for sustainable health monitoring","volume":"21","author":"Chen","year":"2016","journal-title":"Mob. Netw. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Goldstein, M., and Uchida, S. (2016). A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0152173"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.ins.2015.01.010","article-title":"Autonomous data stream clustering implementing split-and-merge concepts\u2014Towards a plug-and-play approach","volume":"304","author":"Lughofer","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_9","unstructured":"Pool, J., and Dally, W.J. (2020, June 18). Learning Both Weights and Connections for Efficient Neural Networks. Advances in Neural Information Processing Systems. Available online: https:\/\/papers.nips.cc\/paper\/5784-learning-both-weights-and-connections-for-efficient-neural-network.pdf."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kang, M., and Kang, J. (2016). Intrusion detection system using deep neural network for in-vehicle network security. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0155781"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1002\/smj.2297","article-title":"Decision making and uncertainty: The role of heuristics and experience in assessing a politically hazardous environment","volume":"36","author":"Maitland","year":"2014","journal-title":"Strateg. Manag. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"10196","DOI":"10.1109\/ACCESS.2018.2890461","article-title":"Searching with direction awareness: Multi-objective genetic algorithm based on angle quantization and crowding distance moga-aqcd","volume":"7","author":"Metiaf","year":"2018","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"14642","DOI":"10.1109\/ACCESS.2020.2966712","article-title":"Electric load forecasting by hybrid self-recurrent support vector regression model with variational mode decomposition and improved cuckoo search algorithm","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","first-page":"58","article-title":"Hybrid algorithm of Cuckoo Search and Particle Swarm Optimization","volume":"7","author":"Kundra","year":"2015","journal-title":"Res. J. Inf. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"960","DOI":"10.3390\/en4060960","article-title":"SVR with hybrid chaotic immune algorithm for seasonal load demand forecasting","volume":"4","author":"Hong","year":"2011","journal-title":"Energies"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1109\/TSMC.2017.2757029","article-title":"Self-adaptive framework for efficient stream data classification on storm","volume":"50","author":"Deng","year":"2020","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.patcog.2018.11.006","article-title":"Incremental semi-supervised learning on streaming data","volume":"88","author":"Li","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neucom.2018.05.130","article-title":"Data stream classification using active learned neural networks","volume":"353","author":"Ksieniewicz","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.inffus.2018.01.003","article-title":"An iterative boosting-based ensemble for streaming data classification","volume":"45","author":"Junior","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S0218213019600091","article-title":"Data stream classification by dynamic incremental semi-supervised fuzzy clustering","volume":"28","author":"Casalino","year":"2019","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/dac.3002","article-title":"An incremental intrusion detection system using a new semi-supervised stream classification method","volume":"30","author":"Noorbehbahani","year":"2017","journal-title":"Int. J. Commun. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1016\/j.asoc.2017.11.008","article-title":"Large-scale cyber attacks monitoring using Evolving Cauchy Possibilistic Clustering","volume":"62","author":"Skrjanc","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s10844-015-0358-3","article-title":"A grid density based framework for classifying streaming data in the presence of concept drift","volume":"46","author":"Sethi","year":"2016","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.1109\/TCYB.2018.2822552","article-title":"Ant colony stream clustering: A fast density clustering algorithm for dynamic data streams","volume":"49","author":"Fahy","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_25","unstructured":"Fahy, C., and Yang, S. (2019). Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams. IEEE Trans. Big Data."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2871","DOI":"10.1109\/TKDE.2016.2594068","article-title":"An optimization model for clustering categorical data streams with drifting concepts","volume":"28","author":"Bai","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.jnca.2014.11.007","article-title":"MuDi-Stream: A multi density clustering algorithm for evolving data stream","volume":"59","author":"Amini","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_28","first-page":"232","article-title":"On-Line Sequential Extreme Learning Machine Review of Extreme Learning Ma- Proposed Online Sequential Ex- treme Learning Machine","volume":"2005","author":"Huang","year":"2005","journal-title":"Comput. Intell."},{"key":"ref_29","first-page":"4610","article-title":"Extreme learning machine: A review","volume":"12","author":"Abbas","year":"2017","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.neunet.2014.10.001","article-title":"Trends in extreme learning machines: A review","volume":"61","author":"Huang","year":"2015","journal-title":"Neural Netw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1109\/ACCESS.2015.2450498","article-title":"High-performance extreme learning machines: A complete toolbox for big data applications","volume":"3","author":"Akusok","year":"2015","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4429","DOI":"10.1021\/acs.analchem.7b04399","article-title":"Consensus classification using non-optimized classifiers","volume":"90","author":"Brownfield","year":"2018","journal-title":"Anal. Chem."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/TNN.2006.882812","article-title":"A kernel-based two-class classifier for imbalanced data sets","volume":"18","author":"Hong","year":"2007","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_34","unstructured":"Joshi, M.V. (2002, January 9\u201312). On Evaluating Performance of Classifiers for Rare Classes. Proceedings of the 2002 IEEE International Conference on Data Mining, ICDM, Maebashi City, Japan."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lan, Y., Wang, Q., Cole, J.R., and Rosen, G.L. (2012). Using the RDP classifier to predict taxonomic novelty and reduce the search space for finding novel organisms. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0032491"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Seliya, N., Khoshgoftaar, T.M., and Van Hulse, J. (2009, January 2\u20134). A Study on the Relationships of Classifier Performance Metrics. Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence ICTAI, Newark, NJ, USA.","DOI":"10.1109\/ICTAI.2009.25"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A.A. (2009, January 8\u201310). A Detailed Analysis of the KDD CUP 99 Data Set. Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada.","DOI":"10.1109\/CISDA.2009.5356528"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/8\/1292\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:54:05Z","timestamp":1760176445000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/8\/1292"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,4]]},"references-count":37,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["sym12081292"],"URL":"https:\/\/doi.org\/10.3390\/sym12081292","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2020,8,4]]}}}