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Han H, Sun C, Wu X, Yang H, Qiao J. Self-Organizing Interval Type-2 Fuzzy Neural Network Using Information Aggregation Method. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:6428-6442. [PMID: 34982701 DOI: 10.1109/tnnls.2021.3136678] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Interval type-2 fuzzy neural networks (IT2FNNs) usually stack adequate fuzzy rules to identify nonlinear systems with high-dimensional inputs, which may result in an explosion of fuzzy rules. To cope with this problem, a self-organizing IT2FNN, based on the information aggregation method (IA-SOIT2FNN), is developed to avoid the explosion of fuzzy rules in this article. First, a relation-aware strategy is proposed to construct rotatable type-2 fuzzy rules (RT2FRs). This strategy uses the individual RT2FR, instead of multiple standard fuzzy rules, to interpret interactive features of high-dimensional inputs. Second, a comprehensive information evaluation mechanism, associated with the interval information and rotation information of RT2FR, is developed to direct the structural adjustment of IA-SOIT2FNN. This mechanism can achieve a compact structure of IA-SOIT2FNN by growing and pruning RT2FRs. Third, a multicriteria-based optimization algorithm is designed to optimize the parameters of IA-SOIT2FNN. The algorithm can simultaneously update the rotatable parameters and the conventional parameters of RT2FR, and further maintain the accuracy of IA-SOIT2FNN. Finally, the experiments showcase that the proposed IA-SOIT2FNN can compete with the state-of-the-art approaches in terms of identification performance.
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de Campos Souza PV, Lughofer E. Online active learning for an evolving fuzzy neural classifier based on data density and specificity. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.09.133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Mengi M, Malhotra D. A systematic literature review on traditional to artificial intelligence based socio-behavioral disorders diagnosis in India: Challenges and future perspectives. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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de Campos Souza PV, Lughofer E. Evolving fuzzy neural classifier that integrates uncertainty from human-expert feedback. EVOLVING SYSTEMS 2022; 14:319-341. [PMID: 37009465 PMCID: PMC10061807 DOI: 10.1007/s12530-022-09455-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 07/17/2022] [Indexed: 10/15/2022]
Abstract
AbstractEvolving fuzzy neural networks are models capable of solving complex problems in a wide variety of contexts. In general, the quality of the data evaluated by a model has a direct impact on the quality of the results. Some procedures can generate uncertainty during data collection, which can be identified by experts to choose more suitable forms of model training. This paper proposes the integration of expert input on labeling uncertainty into evolving fuzzy neural classifiers (EFNC) in an approach called EFNC-U. Uncertainty is considered in class label input provided by experts, who may not be entirely confident in their labeling or who may have limited experience with the application scenario for which the data is processed. Further, we aimed to create highly interpretable fuzzy classification rules to gain a better understanding of the process and thus to enable the user to elicit new knowledge from the model. To prove our technique, we performed binary pattern classification tests within two application scenarios, cyber invasion and fraud detection in auctions. By explicitly considering class label uncertainty in the update process of the EFNC-U, improved accuracy trend lines were achieved compared to fully (and blindly) updating the classifiers with uncertain data. Integration of (simulated) labeling uncertainty smaller than 20% led to similar accuracy trends as using the original streams (unaffected by uncertainty). This demonstrates the robustness of our approach up to this uncertainty level. Finally, interpretable rules were elicited for a particular application (auction fraud identification) with reduced (and thus readable) antecedent lengths and with certainty values in the consequent class labels. Additionally, an average expected uncertainty of the rules were elicited based on the uncertainty levels in those samples which formed the corresponding rules.
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Kafiyan-Safari M, Rouhani M. Adaptive one-pass passive-aggressive radial basis function for classification problems. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.03.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Gu X, Shen Q, Angelov PP. Particle Swarm Optimized Autonomous Learning Fuzzy System. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:5352-5363. [PMID: 32092025 DOI: 10.1109/tcyb.2020.2967462] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The antecedent and consequent parts of a first-order evolving intelligent system (EIS) determine the validity of the learning results and overall system performance. Nonetheless, the state-of-the-art techniques mostly stress on the novelty from the system identification point of view but pay less attention to the optimality of the learned parameters. Using the recently introduced autonomous learning multiple model (ALMMo) system as the implementation basis, this article introduces a particle swarm-based approach for the EIS optimization. The proposed approach is able to simultaneously optimize the antecedent and consequent parameters of ALMMo and effectively enhance the system performance by iteratively searching for optimal solutions in the problem spaces. In addition, the proposed optimization approach does not adversely influence the "one pass" learning ability of ALMMo. Once the optimization process is complete, ALMMo can continue to learn from new data to incorporate unseen data patterns recursively without full retraining. The experimental studies with a number of real-world benchmark problems validate the proposed concept and general principles. It is also verified that the proposed optimization approach can be applied to other types of EISs with similar operating mechanisms.
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Gu X, Angelov P, Zhao Z. Self-organizing fuzzy inference ensemble system for big streaming data classification. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106870] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Škrjanc I, Iglesias JA, Sanchis A, Leite D, Lughofer E, Gomide F. Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.03.060] [Citation(s) in RCA: 151] [Impact Index Per Article: 30.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Han M, Zhong K, Qiu T, Han B. Interval Type-2 Fuzzy Neural Networks for Chaotic Time Series Prediction: A Concise Overview. IEEE TRANSACTIONS ON CYBERNETICS 2019; 49:2720-2731. [PMID: 29993733 DOI: 10.1109/tcyb.2018.2834356] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Chaotic time series widely exists in nature and society (e.g., meteorology, physics, economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent nonstationary and high complexity. Thankfully, multifarious advanced approaches have been developed to tackle the prediction issues, such as statistical methods, artificial neural networks (ANNs), and support vector machines. Among them, the interval type-2 fuzzy neural network (IT2FNN), which is a synergistic integration of fuzzy logic systems and ANNs, has received wide attention in the field of chaotic time series prediction. This paper begins with the structural features and superiorities of IT2FNN. Moreover, chaotic characters identification and phase-space reconstruction matters for prediction are presented. In addition, we also offer a comprehensive review of state-of-the-art applications of IT2FNN, with an emphasis on chaotic time series prediction and summarize their main contributions as well as some hardware implementations for computation speedup. Finally, this paper trends and extensions of this field, along with an outlook of future challenges are revealed. The primary objective of this paper is to serve as a tutorial or referee for interested researchers to have an overall picture on the current developments and identify their potential research direction to further investigation.
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Luo C, Tan C, Wang X, Zheng Y. An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.02.032] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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A non-singleton type-2 fuzzy neural network with adaptive secondary membership for high dimensional applications. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.01.095] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Towards the use of fuzzy logic systems in rotary wing unmanned aerial vehicle: a review. Artif Intell Rev 2018. [DOI: 10.1007/s10462-018-9653-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Han HG, Chen ZY, Liu HX, Qiao JF. A self-organizing interval Type-2 fuzzy-neural-network for modeling nonlinear systems. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.02.049] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Lughofer E. On-line active learning: A new paradigm to improve practical useability of data stream modeling methods. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2017.06.038] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Pratama M, Lughofer E, Er MJ, Anavatti S, Lim CP. Data driven modelling based on Recurrent Interval-Valued Metacognitive Scaffolding Fuzzy Neural Network. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.10.093] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Pratama M, Lu J, Lughofer E, Zhang G, Anavatti S. Scaffolding type-2 classifier for incremental learning under concept drifts. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.01.049] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Das AK, Anh N, Suresh S, Srikanth N. An interval type-2 fuzzy inference system and its meta-cognitive learning algorithm. EVOLVING SYSTEMS 2016. [DOI: 10.1007/s12530-016-9148-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Levman J, Takahashi E. Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders. Neuroimage Clin 2015; 9:532-44. [PMID: 26640765 PMCID: PMC4625213 DOI: 10.1016/j.nicl.2015.09.017] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2015] [Revised: 09/23/2015] [Accepted: 09/25/2015] [Indexed: 01/15/2023]
Abstract
Multivariate analysis (MVA) is a class of statistical and pattern recognition methods that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of medical neuroimaging-related challenges including identifying variables associated with a measure of clinical importance (i.e. patient outcome), creating diagnostic tests, assisting in characterizing developmental disorders, understanding disease etiology, development and progression, assisting in treatment monitoring and much more. Compared to adults, imaging of developing immature brains has attracted less attention from MVA researchers. However, remarkable MVA research growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to neurodevelopmental disorders in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. The goal of this manuscript is to provide a concise review of the state of the scientific literature on studies employing brain MRI and MVA in a pre-adult population. Neurological developmental disorders addressed in the MVA research contained in this review include autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia and more. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in pediatric/neonatal/fetal brain MRI, the field is still young and considerable research growth remains ahead of us.
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Affiliation(s)
- Jacob Levman
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street #456, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street #456, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
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