Relevant bibliographies by topics / Classifier ensemble construction

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  1. Journal articles
  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Classifier ensemble construction'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Classifier ensemble construction"

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Husin, Abdullah, and Ku Ruhana Ku-Mahamud. "Ant System and Weighted Voting Method for Multiple Classifier Systems." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4705–12. https://doi.org/10.11591/ijece.v8i6.pp4705-4712.

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Combining multiple classifiers is considered as a general solution for classification tasks. However, there are two problems in combining multiple classifiers: constructing a diverse classifier ensemble; and, constructing an appropriate combiner. In this study, an improved multiple classifier combination scheme is propose. A diverse classifier ensemble is constructed by training them with different feature set partitions. The ant system-based algorithm is used to form the optimal feature set partitions. Weighted voting is used to combine the classifiers’ outputs by considering the streng
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AKHAND, M. A. H., MD MONIRUL ISLAM, and KAZUYUKI MURASE. "A COMPARATIVE STUDY OF DATA SAMPLING TECHNIQUES FOR CONSTRUCTING NEURAL NETWORK ENSEMBLES." International Journal of Neural Systems 19, no. 02 (2009): 67–89. http://dx.doi.org/10.1142/s0129065709001859.

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Ensembles with several classifiers (such as neural networks or decision trees) are widely used to improve the generalization performance over a single classifier. Proper diversity among component classifiers is considered an important parameter for ensemble construction so that failure of one may be compensated by others. Among various approaches, data sampling, i.e., different data sets for different classifiers, is found more effective than other approaches. A number of ensemble methods have been proposed under the umbrella of data sampling in which some are constrained to neural networks or
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Bentkowska, Urszula, Wojciech Gałka, Marcin Mrukowicz, and Aleksander Wojtowicz. "Ensemble Classifier Based on Interval Modeling for Microarray Datasets." Entropy 26, no. 3 (2024): 240. http://dx.doi.org/10.3390/e26030240.

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The purpose of the study is to propose a multi-class ensemble classifier using interval modeling dedicated to microarray datasets. An approach of creating the uncertainty intervals for the single prediction values of constituent classifiers and then aggregating the obtained intervals with the use of interval-valued aggregation functions is used. The proposed heterogeneous classification employs Random Forest, Support Vector Machines, and Multilayer Perceptron as component classifiers, utilizing cross-entropy to select the optimal classifier. Moreover, orders for intervals are applied to determ
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Shibaikin, Sergei, Vladimir Nikulin, and Andrei Abbakumov. "Analysis of machine learning methods for computer systems to ensure safety from fraudulent texts." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2020, no. 1 (2020): 29–40. http://dx.doi.org/10.24143/2072-9502-2020-1-29-40.

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IT Security is an essential condition for functioning of each company whose work is related to the information storage. Various models for detecting fraudulent texts including a support vector machine, neural networks, logistic regression, and a naive Bayes classifier, have been analyzed. It is proposed to increase the efficiency of detection of fraudulent messages by combining classifiers in ensembles. The metaclassifier allows to consider the accuracy values of all analyzers, involving in the work the construction of the weight matrix and the characteristic that determines the minimum accura
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Troć, Maciej, and Olgierd Unold. "Self-adaptation of parameters in a learning classifier system ensemble machine." International Journal of Applied Mathematics and Computer Science 20, no. 1 (2010): 157–74. http://dx.doi.org/10.2478/v10006-010-0012-8.

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Self-adaptation of parameters in a learning classifier system ensemble machineSelf-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem of EAs. This wo
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Mostofi, Fatemeh, Vedat Toğan, Yunus Ayözen, and Onur Behzat Tokdemir. "Predicting the Impact of Construction Rework Cost Using an Ensemble Classifier." Sustainability 14, no. 22 (2022): 14800. http://dx.doi.org/10.3390/su142214800.

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Predicting construction cost of rework (COR) allows for the advanced planning and prompt implementation of appropriate countermeasures. Studies have addressed the causation and different impacts of COR but have not yet developed the robust cost predictors required to detect rare construction rework items with a high-cost impact. In this study, two ensemble learning methods (soft and hard voting classifiers) are utilized for nonconformance construction reports (NCRs) and compared with the literature on nine machine learning (ML) approaches. The ensemble voting classifiers leverage the advantage
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Md. Farid, Dewan, Mohammad Zahidur Rahman, and Chowdhury Mofizur Rahman. "An Ensemble Approach to Classifier Construction based on Bootstrap Aggregation." International Journal of Computer Applications 25, no. 5 (2011): 30–34. http://dx.doi.org/10.5120/3027-4098.

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ASKER, LARS, MATS DANIELSON, and LOVE EKENBERG. "COMMITTEES OF LEARNING AGENTS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 02 (2000): 187–202. http://dx.doi.org/10.1142/s0218488500000137.

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We describe how machine learning and decision theory is combined in an application that supports control room operators of a combined heating and power plant to cope with the overwhelming complexity of situations when severe plant disturbances occur. The application is designed as an assistant, rather than as an automatic system that intervenes directly in the operator/plant loop. The application is required to handle vague and numerically imprecise background information in the construction of classifier committees. A classifier committee (or ensemble) is a classifier created by combining the
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Amirgaliyev, Ye, V. Berikov, L. Cherikbayeva, B. Tulegenova, and E. Daiyrbayeva. "CONSTRUCTION OF AN OPTIMAL COLLECTIVE DECISION ON THE BASIS OF A CLUSTER ENSEMBLE." BULLETIN Series of Physics & Mathematical Sciences 76, no. 4 (2021): 65–71. http://dx.doi.org/10.51889/2021-4.1728-7901.09.

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This article presents methods of image analysis based on supervised learning and an algorithm consisting of two stages of determining the optimal classifier using a cluster ensemble. At the first stage, the averaged co-association matrix is calculated using a cluster ensemble. In the clustering ensemble, we used a scheme of a single clustering algorithm that constructs base partitions with parameters taken at random. At the second stage, the optimal classifier is determined using the resulting kernel matrix as input data. Numerical experiments were carried out with real hyperspectral images. T
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Amirgaliyev, Ye, V. Berikov, L. Cherikbayeva, B. Tulegenova, and E. Daiyrbayeva. "CONSTRUCTION OF AN OPTIMAL COLLECTIVE DECISION ON THE BASIS OF A CLUSTER ENSEMBLE." BULLETIN Series of Physics & Mathematical Sciences 76, no. 4 (2021): 65–71. http://dx.doi.org/10.51889/2021-4.1728-7901.09.

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This article presents methods of image analysis based on supervised learning and an algorithm consisting of two stages of determining the optimal classifier using a cluster ensemble. At the first stage, the averaged co-association matrix is calculated using a cluster ensemble. In the clustering ensemble, we used a scheme of a single clustering algorithm that constructs base partitions with parameters taken at random. At the second stage, the optimal classifier is determined using the resulting kernel matrix as input data. Numerical experiments were carried out with real hyperspectral images. T
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Dissertations / Theses on the topic "Classifier ensemble construction"

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Lima, Tiago Pessoa Ferreira de. "An authomatic method for construction of multi-classifier systems based on the combination of selection and fusion." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/12457.

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Submitted by João Arthur Martins (joao.arthur@ufpe.br) on 2015-03-12T17:38:41Z No. of bitstreams: 2 Dissertaçao Tiago de Lima.pdf: 1469834 bytes, checksum: 95a0326778b3d0f98bd35a7449d8b92f (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5)<br>Approved for entry into archive by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-13T14:23:38Z (GMT) No. of bitstreams: 2 Dissertaçao Tiago de Lima.pdf: 1469834 bytes, checksum: 95a0326778b3d0f98bd35a7449d8b92f (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5)<br>Made available in DSpace
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Gacquer, David. "Sur l'utilisation active de la diversité dans la construction d'ensembles de classifieurs. Application à la détection de fumées nocives sur site industriel." Phd thesis, Université de Valenciennes et du Hainaut-Cambresis, 2008. http://tel.archives-ouvertes.fr/tel-00392616.

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L'influence de la diversité lors de la construction d'ensembles de classifieurs a soulevé de nombreuses discussions au sein de la communauté de l'Apprentissage Automatique ces dernières années. <br> Une manière particulière de construire un ensemble de classifieurs consiste à sélectionner individuellement les membres de l'ensemble à partir d'un pool de classifieurs en se basant sur des critères prédéfinis. <br> La littérature fait référence à cette méthode sous le terme de paradigme Surproduction et Sélection, également appelé élagage d'ensemble de classifieurs.<br> <br> Les travaux présentés
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Bernard, Simon. "Forêts Aléatoires: De l'Analyse des Mécanismes de Fonctionnement à la Construction Dynamique." Phd thesis, Université de Rouen, 2009. http://tel.archives-ouvertes.fr/tel-00598441.

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Les travaux de cette thèse se situent dans le domaine de l'apprentissage automatique et concernent plus particulièrement la paramétrisation des forêts aléatoires, une technique d'ensembles de classifieurs utilisant des arbres de décision. Nous nous intéressons à deux paramètres importants pour l'induction de ces forêts: le nombre de caractéristiques choisies aléatoirement à chaque noeud et le nombre d'arbres. Nous montrons d'abord que la valeur du premier paramètre doit être choisie en fonction des propriétés de l'espace de description, et proposons dans ce cadre un nouvel algorithme nommé For
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Bernard, Simon. "Forêts aléatoires : de l’analyse des mécanismes de fonctionnement à la construction dynamique." Phd thesis, Rouen, 2009. http://www.theses.fr/2009ROUES011.

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Les travaux de cette thèse se situent dans le domaine de l’apprentissage automatique et concernent plus particulièrement la paramétrisation des forêts aléatoires, une technique d’ensembles de classifieurs utilisant des arbres de décision. Nous nous intéressons à deux paramètres importants pour l’induction de ces forêts : le nombre de caractéristiques choisies aléatoirement à chaque noeud et le nombre d’arbres. Nous montrons d’abord que la valeur du premier paramètre doit être choisie en fonction des propriétés de l’espace de description, et proposons dans ce cadre un nouvel algorithme nommé Fo
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Book chapters on the topic "Classifier ensemble construction"

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Christensen, Stefan W. "Ensemble Construction via Designed Output Distortion." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44938-8_29.

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Sułot, Dominika, Paweł Zyblewski, and Paweł Ksieniewicz. "Analysis of Variance Application in the Construction of Classifier Ensemble Based on Optimal Feature Subset for the Task of Supporting Glaucoma Diagnosis." In Computational Science – ICCS 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77967-2_10.

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Jȩdrzejowicz, Joanna, and Piotr Jȩdrzejowicz. "Constructing Ensemble Classifiers from GEP-Induced Expression Trees." In Next Generation Data Technologies for Collective Computational Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20344-2_7.

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Shao, Yan, Zhanjun Li, and Ming Li. "A New Method for Constructing Ensemble Classifier in Privacy-Preserving Distributed Environment." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68759-9_55.

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Abawajy, Jemal, and Andrei Kelarev. "A Multi-tier Ensemble Construction of Classifiers for Phishing Email Detection and Filtering." In Cyberspace Safety and Security. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35362-8_5.

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Mirzaei, H., and M. Jafarzadegan. "Constructing Ensemble-Based Classifiers Based on Feature Transformation: Application in Hand Recognition." In Human-Computer Systems Interaction. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03202-8_36.

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Liu, Wei, Harrison J. Sweeney, Bo Chung, and David G. Glance. "Constructing Consumer-Oriented Medical Terminology from the Web A Supervised Classifier Ensemble Approach." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13560-1_61.

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Cheriguene, Soraya, Nabiha Azizi, and Nilanjan Dey. "Ensemble Classifiers Construction Using Diversity Measures and Random Subspace Algorithm Combination: Application to Glaucoma Diagnosis." In Medical Imaging in Clinical Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33793-7_6.

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Li, Dong, and Xiaobo Peng. "Research on EEG Feature Extraction and Recognition Method of Lower Limb Motor Imagery." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_121.

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AbstractAiming at the problems of difficult signal acquisition, low signal-to-noise ratio and poor classification accuracy of BCI technology, based on the theory of EEG, this paper designs a leg raising EEG experiment of lower limb motor imagery and collects EEG signal data from 20 subjects to improve the accuracy of classification and recognition The process of feature extraction and classification recognition is explored, and a multi domain fusion method is proposed for EEG signal feature extraction from time domain, frequency domain, time-frequency domain and spatial domain. At the same tim
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Eeti, Laxmi Narayana, and Krishna Mohan Buddhiraju. "Perspective Based Model for Constructing Diverse Ensemble Members in Multi-classifier Systems for Multi-spectral Image Classification." In Advanced Information Systems Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-319-12568-8_78.

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Conference papers on the topic "Classifier ensemble construction"

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Andronati, Oleksandr, Svitlana Antoshchuk, Oksana Babilunha, Olena Arsirii, Anatolii Nikolenko, and Kyrylo Mikhalev. "A Method of Constructing Ensemble Classifiers for Recognizing Audio Data of Various Nature." In 2024 14th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2024. http://dx.doi.org/10.1109/acit62333.2024.10712469.

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Azizi, Nabiha, Nadir Farah, and Mokhtar Sellami. "Ensemble classifier construction for Arabic handwritten recongnition." In 2011 7th International Workshop on Systems, Signal Processing and their Applications (WOSSPA). IEEE, 2011. http://dx.doi.org/10.1109/wosspa.2011.5931470.

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Cross, Valerie, and Michael Zmuda. "Ensemble Creation using Fuzzy Similarity Measures and Feature Subset Evaluators." In 2nd International Conference on Machine Learning Techniques and NLP (MLNLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111407.

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Current machine learning research is addressing the problem that occurs when the data set includes numerous features but the number of training data is small. Microarray data, for example, typically has a very large number of features, the genes, as compared to the number of training data examples, the patients. An important research problem is to develop techniques to effectively reduce the number of features by selecting the best set of features for use in a machine learning process, referred to as the feature selection problem. Another means of addressing high dimensional data is the use of
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Lee, Michael C., Lilla Boroczky, Kivilcim Sungur-Stasik, et al. "A Two-Step Approach for Feature Selection and Classifier Ensemble Construction in Computer-Aided Diagnosis." In 2008 21st International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2008. http://dx.doi.org/10.1109/cbms.2008.68.

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Gao, Zhen, Maryam Zand, and Jianhua Ruan. "A Novel Multiple Classifier Generation and Combination Framework Based on Fuzzy Clustering and Individualized Ensemble Construction." In 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2019. http://dx.doi.org/10.1109/dsaa.2019.00038.

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Formentin, Nathan, Eduardo Borges, Giancarlo Lucca, Helida Santos, and Gracaliz Dimuro. "Death Registry Prediction in Brazilian Male Prisons with a Random Forest Ensemble." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/eniac.2020.12140.

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Brazil has the third-largest prison population globally, and it has been growing steadily for more than two decades. Constant growth and low jail investment generated significant problems, such as overcrowding and widespread diseases. This study proposes the construction of a Random Forest classifier to predict the occurrence of deaths in prisons. We extracted data from the National Survey of Penitentiary Information for the years 2015 to 2016. The best-fitted classifier achieved accuracy equals 87% being able to identify correctly up to 84% of deaths occurrences. In the present work, it was p
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Jedrzejowicz, Joanna, and Piotr Jedrzejowicz. "Constructing Ensemble Classifiers from Expression Trees." In 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE, 2010. http://dx.doi.org/10.1109/3pgcic.2010.48.

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Bernardini, F. C., M. C. Monard, and R. C. Prati. "Constructing ensembles of symbolic classifiers." In Fifth International Conference on Hybrid Intelligent Systems (HIS'05). IEEE, 2005. http://dx.doi.org/10.1109/ichis.2005.31.

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Yun Zhai, Bingru Yang, Nan Ma, and Da Ruan. "New construction of Ensemble Classifiers for imbalanced datasets." In 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2010. http://dx.doi.org/10.1109/iske.2010.5680874.

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Xu, Xikai, Jing Dong, Wei Wang, and Tieniu Tan. "Robust steganalysis based on training set construction and ensemble classifiers weighting." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351050.

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