{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T05:20:39Z","timestamp":1761110439778,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,4,22]],"date-time":"2019-04-22T00:00:00Z","timestamp":1555891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>During the last few decades, machine learning has constituted a significant tool in extracting useful knowledge from economic data for assisting decision-making. In this work, we evaluate the performance of weight-constrained recurrent neural networks in forecasting economic classification problems. These networks are efficiently trained with a recently-proposed training algorithm, which has two major advantages. Firstly, it exploits the numerical efficiency and very low memory requirements of the limited memory BFGS matrices; secondly, it utilizes a gradient-projection strategy for handling the bounds on the weights. The reported numerical experiments present the classification accuracy of the proposed model, providing empirical evidence that the application of the bounds on the weights of the recurrent neural network provides more stable and reliable learning.<\/jats:p>","DOI":"10.3390\/a12040085","type":"journal-article","created":{"date-parts":[[2019,4,22]],"date-time":"2019-04-22T11:02:53Z","timestamp":1555930973000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3996-3301","authenticated-orcid":false,"given":"Ioannis E.","family":"Livieris","sequence":"first","affiliation":[{"name":"Department of Computer &amp; Informatics Engineering, Technological Educational Institute of Western Greece, GR 263-34 Antirrio, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.asoc.2011.11.002","article-title":"An artificial immune classifier for credit scoring analysis","volume":"12","author":"Chang","year":"2012","journal-title":"Appl. Soft Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.dss.2014.03.001","article-title":"A data-driven approach to predict the success of bank telemarketing","volume":"62","author":"Moro","year":"2014","journal-title":"Decis. Support Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1016\/j.asoc.2015.09.040","article-title":"Artificial neural networks in business: Two decades of research","volume":"38","author":"Verner","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.neucom.2017.03.085","article-title":"The na\u00efve associative classifier (NAC): A novel, simple, transparent, and accurate classification model evaluated on financial data","volume":"265","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"538","DOI":"10.3390\/a7040538","article-title":"Predicting student academic performance: A comparison of two meta-heuristic algorithms inspired by cuckoo birds for training neural networks","volume":"7","author":"Chen","year":"2014","journal-title":"Algorithms"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Huang, X., and Wang, Z. (2016). Multiple Artificial Neural Networks with Interaction Noise for Estimation of Spatial Categorical Variables. Algorithms, 9.","DOI":"10.3390\/a9030056"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Purnamasari, P., Ratna, A., and Kusumoputro, B. (2017). Development of filtered bispectrum for EEG signal feature extraction in automatic emotion recognition using artificial neural networks. Algorithms, 10.","DOI":"10.3390\/a10020063"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wu, F., Fu, K., Wang, Y., Xiao, Z., and Fu, X. (2017). A spatial-temporal-semantic neural network algorithm for location prediction on moving objects. Algorithms, 10.","DOI":"10.3390\/a10020037"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3390\/make1010006","article-title":"Why topology for machine learning and knowledge extraction?","volume":"1","author":"Ferri","year":"2018","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Suzuki, K. (2013). Artificial Neural Networks-Architectures and Applications, InTechOpen.","DOI":"10.5772\/3409"},{"key":"ref_11","unstructured":"Singh, D., Merdivan, E., Psychoula, I., Kropf, J., Hanke, S., Geist, M., and Holzinger, A. (September, January 29). Human activity recognition using recurrent neural networks. Proceedings of the International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Reggio, Italy."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shanmuganathan, S., and Samarasinghe, S. (2016). Artificial Neural Network Modelling, Springer.","DOI":"10.1007\/978-3-319-28495-8"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Livieris, I.E. (2018). Improving the Classification Efficiency of an ANN Utilizing a New Training Methodology. Informatics, 6.","DOI":"10.3390\/informatics6010001"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s101070100263","article-title":"Benchmarking optimization software with performance profiles","volume":"91","author":"Dolan","year":"2002","journal-title":"Math. Program."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.neucom.2015.10.042","article-title":"A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing","volume":"175","author":"Zakaryazad","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jena, S.K., Kumar, A., and Dwivedy, M. (2017). Banking Credit Scoring Assessment Using Predictive K-Nearest Neighbour (PKNN) Classifier. Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics, IGI Global.","DOI":"10.4018\/978-1-5225-0997-4.ch018"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Livieris, I.E., Kiriakidou, N., Kanavos, A., Tampakas, V., and Pintelas, P. (2018). On Ensemble SSL Algorithms for Credit Scoring Problem. Informatics, 5.","DOI":"10.3390\/informatics5040040"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1145\/279232.279236","article-title":"Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization","volume":"23","author":"Zhu","year":"1997","journal-title":"ACM Trans. Math. Softw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1145\/2049662.2049669","article-title":"Remark on \u201cAlgorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization\u201d","volume":"38","author":"Morales","year":"2011","journal-title":"ACM Trans. Math. Softw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1145\/192115.192132","article-title":"Line search algorithms with guaranteed sufficient decrease","volume":"20","author":"Thuente","year":"1994","journal-title":"ACM Trans. Math. Softw."},{"key":"ref_21","unstructured":"Dua, D., and Karra Taniskidou, E. (2017). UCI Machine Learning Repository, University of California."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5421","DOI":"10.1016\/j.amc.2010.12.012","article-title":"Nonmonotone BFGS-trained recurrent neural networks for temporal sequence processing","volume":"217","author":"Peng","year":"2011","journal-title":"Appl. Math. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1007\/s00521-010-0493-2","article-title":"Nonmonotone Levenberg\u2013Marquardt training of recurrent neural architectures for processing symbolic sequences","volume":"20","author":"Peng","year":"2011","journal-title":"Neural Comput. Appl."},{"key":"ref_24","first-page":"71","article-title":"Improving the learning speed of 2-layer neural network by choosing initial values of adaptive weights","volume":"59","author":"Nguyen","year":"1990","journal-title":"Biol. Cybern."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1023\/A:1007413511361","article-title":"On the optimality of the simple Bayesian classifier under zero-one loss","volume":"29","author":"Domingos","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_26","unstructured":"Kearns, M., Solla, S., and Cohn, D. (1999). Using sparseness and analytic QP to speed training of support vector machines. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_27","unstructured":"Aha, D.W. (2013). Lazy Learning, Springer Science & Business Media."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wu, X., and Kumar, V. (2009). The Top 10 Algorithms in Data Mining, CRC Press.","DOI":"10.1201\/9781420089653"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kolias, V., Kolias, C., Anagnostopoulos, I., and Kayafas, E. (2014, January 27\u201330). RuleMR: Classification rule discovery with MapReduce. Proceedings of the 2014 IEEE International Conference on Big Data (Big Data), Washington, DC, USA.","DOI":"10.1109\/BigData.2014.7004440"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kolias, V., Anagnostopoulos, I., and Kayafas, E. (2014, January 8\u201311). A Covering Classification Rule Induction Approach for Big Datasets. Proceedings of the 2014 IEEE\/ACM International Symposium on Big Data Computing, London, UK.","DOI":"10.1109\/BDC.2014.17"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/4\/85\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:46:18Z","timestamp":1760186778000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/4\/85"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,22]]},"references-count":31,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["a12040085"],"URL":"https:\/\/doi.org\/10.3390\/a12040085","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2019,4,22]]}}}