1
|
Xu P, Cho JH, Salado A. Expert Opinion Fusion Framework Using Subjective Logic for Fault Diagnosis. IEEE Trans Cybern 2022; 52:4300-4311. [PMID: 33170790 DOI: 10.1109/tcyb.2020.3025800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Fault diagnosis plays a critical role in maintaining and troubleshooting engineered systems. Various diagnosis models, such as Bayesian networks (BNs), have been proposed to deal with this kind of problem in the past. However, the diagnosis results may not be reliable if second-order uncertainty is involved. This article proposes a hierarchical system diagnosis fusion framework that considers the uncertainty based on a belief model, called subjective logic (SL), which explicitly deals with uncertainty representing a lack of evidence. The proposed system diagnosis fusion framework consists of three steps: 1) individual subjective BNs (SBNs) are designed to represent the knowledge architectures of individual experts; 2) experts are clustered as expert groups according to their similarity; and 3) after inferring expert opinions from respective SBNs, the one opinion fusion method was used to combine all opinions to reach a consensus based on the aggregated opinion for system diagnosis. Via extensive simulation experiments, we show that the proposed fusion framework, consisting of two operators, outperforms the state-of-the-art fusion operator counterparts and has stable performance under various scenarios. Our proposed fusion framework is promising for advancing state-of-the-art fault diagnosis of complex engineered systems.
Collapse
|
2
|
Mao S, Lin W, Jiao L, Gou S, Chen JW. End-to-End Ensemble Learning by Exploiting the Correlation Between Individuals and Weights. IEEE Trans Cybern 2021; 51:2835-2846. [PMID: 31425063 DOI: 10.1109/tcyb.2019.2931071] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Ensemble learning performs better than a single classifier in most tasks due to the diversity among multiple classifiers. However, the enhancement of the diversity is at the expense of reducing the accuracies of individual classifiers in general and, thus, how to balance the diversity and accuracies is crucial for improving the ensemble performance. In this paper, we propose a new ensemble method which exploits the correlation between individual classifiers and their corresponding weights by constructing a joint optimization model to achieve the tradeoff between the diversity and the accuracy. Specifically, the proposed framework can be modeled as a shallow network and efficiently trained by the end-to-end manner. In the proposed ensemble method, not only can a high total classification performance be achieved by the weighted classifiers but also the individual classifier can be updated based on the error of the optimized weighted classifiers ensemble. Furthermore, the sparsity constraint is imposed on the weight to enforce that partial individual classifiers are selected for final classification. Finally, the experimental results on the UCI datasets demonstrate that the proposed method effectively improves the performance of classification compared with relevant existing ensemble methods.
Collapse
|
3
|
Krzhizhanovskaya VV, Závodszky G, Lees MH, Dongarra JJ, Sloot PMA, Brissos S, Teixeira J. Clustering and Weighted Scoring in Geometric Space Support Vector Machine Ensemble for Highly Imbalanced Data Classification. Lecture Notes in Computer Science 2020. [PMCID: PMC7303710 DOI: 10.1007/978-3-030-50423-6_10] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Learning from imbalanced datasets is a challenging task for standard classification algorithms. In general, there are two main approaches to solve the problem of imbalanced data: algorithm-level and data-level solutions. This paper deals with the second approach. In particular, this paper shows a new proposition for calculating the weighted score function to use in the integration phase of the multiple classification system. The presented research includes experimental evaluation over multiple, open-source, highly imbalanced datasets, presenting the results of comparing the proposed algorithm with three other approaches in the context of six performance measures. Comprehensive experimental results show that the proposed algorithm has better performance measures than the other ensemble methods for highly imbalanced datasets.
Collapse
|
4
|
Texier G, Allodji RS, Diop L, Meynard JB, Pellegrin L, Chaudet H. Using decision fusion methods to improve outbreak detection in disease surveillance. BMC Med Inform Decis Mak 2019; 19:38. [PMID: 30837003 PMCID: PMC6402142 DOI: 10.1186/s12911-019-0774-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 02/18/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND When outbreak detection algorithms (ODAs) are considered individually, the task of outbreak detection can be seen as a classification problem and the ODA as a sensor providing a binary decision (outbreak yes or no) for each day of surveillance. When they are considered jointly (in cases where several ODAs analyze the same surveillance signal), the outbreak detection problem should be treated as a decision fusion (DF) problem of multiple sensors. METHODS This study evaluated the benefit for a decisions support system of using DF methods (fusing multiple ODA decisions) compared to using a single method of outbreak detection. For each day, we merged the decisions of six ODAs using 5 DF methods (two voting methods, logistic regression, CART and Bayesian network - BN). Classical metrics of accuracy, prediction and timelines were used during the evaluation steps. RESULTS In our results, we observed the greatest gain (77%) in positive predictive value compared to the best ODA if we used DF methods with a learning step (BN, logistic regression, and CART). CONCLUSIONS To identify disease outbreaks in systems using several ODAs to analyze surveillance data, we recommend using a DF method based on a Bayesian network. This method is at least equivalent to the best of the algorithms considered, regardless of the situation faced by the system. For those less familiar with this kind of technique, we propose that logistic regression be used when a training dataset is available.
Collapse
Affiliation(s)
- Gaëtan Texier
- French Armed Forces Center for Epidemiology and Public Health (CESPA), SSA, Camp de Sainte Marthe, 13568, Marseille, France. .,UMR VITROME, IRD, AP-HM, SSA, IHU-Méditerranée Infection, Aix Marseille Univ, 13005, Marseille, France.
| | - Rodrigue S Allodji
- French Armed Forces Center for Epidemiology and Public Health (CESPA), SSA, Camp de Sainte Marthe, 13568, Marseille, France.,CESP, Univ. Paris-Sud, UVSQ, INSERM, Université Paris-Saclay, Villejuif, France.,Cancer and Radiation Team, Gustave Roussy Cancer Center, F-94805, Villejuif, France
| | - Loty Diop
- International Food Policy Research Institute (IFPRI), Regional Office for West and Central Africa Regional Office, 24063, Dakar, Sénégal
| | - Jean-Baptiste Meynard
- French Armed Forces Center for Epidemiology and Public Health (CESPA), SSA, Camp de Sainte Marthe, 13568, Marseille, France.,UMR 912 - SESSTIM - INSERM/IRD/Aix-Marseille Université, 13385, Marseille, France
| | - Liliane Pellegrin
- French Armed Forces Center for Epidemiology and Public Health (CESPA), SSA, Camp de Sainte Marthe, 13568, Marseille, France.,UMR VITROME, IRD, AP-HM, SSA, IHU-Méditerranée Infection, Aix Marseille Univ, 13005, Marseille, France
| | - Hervé Chaudet
- French Armed Forces Center for Epidemiology and Public Health (CESPA), SSA, Camp de Sainte Marthe, 13568, Marseille, France.,UMR VITROME, IRD, AP-HM, SSA, IHU-Méditerranée Infection, Aix Marseille Univ, 13005, Marseille, France
| |
Collapse
|
6
|
Perea-Ortega JM, Martín-Valdivia MT, Ureña-López LA, Martínez-Cámara E. Improving polarity classification of bilingual parallel corpora combining machine learning and semantic orientation approaches. ACTA ACUST UNITED AC 2013. [DOI: 10.1002/asi.22884] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- José M. Perea-Ortega
- SINAI research group; Computer Science Department; Campus Las Lagunillas s/n; University of Jaén; 23071; Jaén; Spain
| | - M. Teresa Martín-Valdivia
- SINAI research group; Computer Science Department; Campus Las Lagunillas s/n; University of Jaén; 23071; Jaén; Spain
| | - L. Alfonso Ureña-López
- SINAI research group; Computer Science Department; Campus Las Lagunillas s/n; University of Jaén; 23071; Jaén; Spain
| | - Eugenio Martínez-Cámara
- SINAI research group; Computer Science Department; Campus Las Lagunillas s/n; University of Jaén; 23071; Jaén; Spain
| |
Collapse
|
7
|
Su Q, Lu WC, Niu B, Liu X, Gu TH. Classification of the Toxicity of Some Organic Compounds to Tadpoles (Rana Temporaria
) Through Integrating Multiple Classifiers. Mol Inform 2011; 30:672-5. [PMID: 27467259 DOI: 10.1002/minf.201000129] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2010] [Accepted: 06/27/2011] [Indexed: 11/05/2022]
Affiliation(s)
- Qiang Su
- College of Material Science and Engineering, Shanghai University, Shanghai, 2000444, China
| | - Wen-Cong Lu
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China tel.: +86 21 6613 2663; fax: +86 21 66134080.
| | - Bing Niu
- College of Life Sciences, Shanghai University, Shanghai, 2000444, China tel.: +86 21 6613 7038; fax: +86 21 66134080.
| | - Xu Liu
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China tel.: +86 21 6613 2663; fax: +86 21 66134080
| | - Tian-Hong Gu
- College of Material Science and Engineering, Shanghai University, Shanghai, 2000444, China
| |
Collapse
|
8
|
Peng CR, Liu L, Niu B, Lv YL, Li MJ, Yuan YL, Zhu YB, Lu WC, Cai YD. Prediction of RNA-binding proteins by voting systems. J Biomed Biotechnol 2011; 2011:506205. [PMID: 21826121 DOI: 10.1155/2011/506205] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2011] [Revised: 05/12/2011] [Accepted: 05/26/2011] [Indexed: 11/29/2022] Open
Abstract
It is important to identify which proteins can interact with RNA for the purpose of
protein annotation, since interactions between RNA and proteins influence the
structure of the ribosome and play important roles in gene expression. This paper
tries to identify proteins that can interact with RNA using voting systems. Firstly
through Weka, 34 learning algorithms are chosen for investigation. Then simple
majority voting system (SMVS) is used for the prediction of RNA-binding proteins,
achieving average ACC (overall prediction accuracy) value of 79.72% and MCC
(Matthew's correlation coefficient) value of 59.77% for the
independent testing dataset. Then mRMR (minimum redundancy maximum relevance)
strategy is used, which is transferred into algorithm selection. In addition, the
MCC value of each classifier is assigned to be the weight of the
classifier's vote. As a result, best average MCC values are attained
when 22 algorithms are selected and integrated through weighted votes, which are
64.70% for the independent testing dataset, and ACC value is 82.04% at this
moment.
Collapse
|
9
|
Abstract
A novel algorithm for font recognition on a single unknown Chinese character, independent of the identity of the character, is proposed in this paper. We employ a wavelet transform on the character image and extract wavelet features from the transformed image. After a Box-Cox transformation and LDA (Linear Discriminant Analysis) process, the discriminating features for font recognition are extracted and classified through a MQDF (Modified Quadric Distance Function) classifier with only one prototype for each font class. Our experiments show that our algorithm can achieve a recognition rate of 90.28 percent on a single unknown character and 99.01 percent if five characters are used for font recognition. Compared with existing methods, all of which are based on a text block, our method can provide a higher recognition rate and is more flexible and robust, since it is based on a single unknown character. Additionally, our method demonstrates that it is possible to extract subtle yet discriminative signals embedded in a much larger noisy background.
Collapse
Affiliation(s)
- Xiaoqing Ding
- Electronics Engineering Department, Tsinghua University, Beijing, P.R. China.
| | | | | |
Collapse
|