Data preprocessing strategy in constructing convolutional neural network classifier based on constrained particle swarm optimization with fuzzy penalty function

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dc.authoridWitold Pedrycz / 0000-0002-9335-9930en_US
dc.authorscopusidWitold Pedrycz / 56854903200en_US
dc.authorwosidWitold Pedrycz / FPE-7309-2022en_US
dc.contributor.authorZhou, Kun
dc.contributor.authorOh, Sung-Kwun
dc.contributor.authorPedrycz, Witold
dc.contributor.authorQiu, Jianlong
dc.date.accessioned2022-12-08T08:13:47Z
dc.date.available2022-12-08T08:13:47Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractConvolutional neural networks (CNNs) have attracted increasing attention in recent years because of their powerful abilities to extract and represent spatial/temporal information. However, for general data, its features are assumed to have weak or no correlation, and directly applying CNN to classify such data could result in poor classification performance. To address this problem, a combined technique of original data representation method of fuzzy penalty function-based constrained particle swarm optimization (FCPSO) and CNN, so-called FCPSO-CNN is designed to effectively solve the classification problems for generic dataset and applied to recognize (classify) black plastic wastes in recycling problems. In more detail, CPSO is introduced to optimize feature reordering matrix under constraints and the construction of this matrix is driven by fitness function of CNN that quantifies classification performance. The Mamdani type fuzzy inference system (FIS) is employed to realize the fuzzy penalty function (FPF) which is utilized to realize the constrained problems of CPSO as well as alleviate the issues of the original penalty function method suffering from the lack of robustness. Experimental results demonstrate that FCPSO-CNN achieves the best classification accuracy on 13 out of 17 datasets; the statistical analysis also confirms the superiority of FCPSO-CNN. An interesting point is worth to mention that some feature reordering matrices in the infeasible space come with better classification accuracy. It has been found that the proposed method results in more accurate solution than one-dimensional CNN, random reordering feature-based CNN and some well-known classifiers (e.g., Naive Bayes, Multilayer perceptron, Support vector machine).en_US
dc.identifier.citationZhou, K., Oh, S. K., Pedrycz, W., & Qiu, J. (2023). Data preprocessing strategy in constructing convolutional neural network classifier based on constrained particle swarm optimization with fuzzy penalty function. Engineering Applications of Artificial Intelligence, 117, 105580.en_US
dc.identifier.doi10.1016/j.engappai.2022.105580en_US
dc.identifier.issn0303-7207en_US
dc.identifier.scopus2-s2.0-85141919314en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.engappai.2022.105580
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3429
dc.identifier.volume117en_US
dc.identifier.wosWOS:000894963300002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPedrycz, Witold
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCNNen_US
dc.subjectFuzzy Penalty FunctionMamdani Type Fuzzy İnference Systemen_US
dc.subjectConstrained Particle Swarm Optimizationen_US
dc.titleData preprocessing strategy in constructing convolutional neural network classifier based on constrained particle swarm optimization with fuzzy penalty functionen_US
dc.typeArticleen_US

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