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fuzzy rule-based classification system using multi-population quantum evolutionary algorithm with contradictory rule reconstruction

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APPLIED INTELLIGENCE 2019年第11期49卷 4007-4021页

作者： Zhang, YuXian Qian, XiaoYi Wang, Jianhui Gendeel, Mohammed Shenyang Univ Technol Sch Elect Engn Shenyang Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Peoples R China

fuzzy rule-based classification systems (FRBCSs) are appropriate tools for dealing with classification problems because of their interpretable models based on linguistic variables. Intelligent optimization techniques are widely used in the rule mining of FRBCSs due to their parallel processing capability for solving the combination optimization of fuzzy antecedent parameters and "don't care" variables, however, FRBCSs suffer from misclassification because of the chain coding scheme and non-guidance of updating strategy in rule mining process. This study presents an FRBCS that uses a multi-population quantum evolutionary algorithm with contradictory rule reconstruction. The developed method utilizes fuzzy C-means clustering to heuristically generate representative initial rules, and performs multi-population quantum coding and guided updating to optimize fuzzy rules. Furthermore, contradictory rule reconstruction is introduced to adjust misclassification rules. Numerical experiment results show that the classification accuracy and noise tolerance of the proposed method are better than those of compared FRBCSs. The proposed method is verified by applying it to fault identification in a wind turbine.

关键词： fuzzy rule-based classification system Multi-population quantum coding Hybrid updating strategy Contradictory rule reconstruction Fault identification

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A fuzzy rule-based classification system Using Interval Type-2 fuzzy Sets

A Fuzzy Rule-Based Classification System Using Interval Type...

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International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2011)

作者： Tang, Min Chen, Xia Hu, Weidong Yu, Wenxian Natl Univ Def Technol ATR Key Lab Changsha 410073 Hunan Peoples R China Shanghai Jiao Tong Univ Sch ElectInformat & Elect Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Sch Elect Engn Shanghai 200030 Peoples R China

ISBN: (纸本)9783642249174

The design of type-2 fuzzy rule-based classification systems from labeled data is considered in this study. With the aid of interval type-2 fuzzy sets, which can effectively capture uncertainties in the data, a compact and interpretable interval type-2 fuzzy rule base with fewer rules is constructed. Corresponding type-2 fuzzy reasoning method for classification is also presented. The validity of this classification system is shown through experimental results on several data sets.

关键词： fuzzy rule-based classification system interval type-2 fuzzy set type-2 fuzzy rule base

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A Multi-objective Evolutionary Algorithm for Tuning Type-2 fuzzy Sets with rule and Condition Selection on fuzzy rule-based classification system 10th

A Multi-objective Evolutionary Algorithm for Tuning Type-2 F...

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10th Conference of the European-Society-for-fuzzy-Logic-and-Technology (EUSFLAT) / 16th International Workshop on Intuitionistic fuzzy Sets and Generalized Nets (IWIFSGN)

作者： Cardenas, Edward Hinojosa Camargo, Heloisa A. Univ San Agustin Arequipa Peru Univ Fed Sao Carlos Sao Carlos SP Brazil

ISBN: (纸本)9783319668307;9783319668291

This paper presents a Multi-Objective Evolutionary Algorithm (MOEA) for tuning type-2 fuzzy sets and selecting rules and conditions on fuzzy rule-based classification systems (FRBCS). Before the tuning and selection process, the rule Base is learned by means of a modified Wang-Mendel algorithm that considers type-2 fuzzy sets in the rules antecedents and in the inference mechanism. The Multi-Objective Evolutionary Algorithm used in the tuning process has three objectives. The first objective reflects the accuracy where the correct classification rate of the FRBCS is optimized. The second objective reflects the interpretability of the system regarding complexity, by means of the quantity of rules and is to be minimized through selecting rules from the initial rule base. The third objective also reflects the interpretability as a matter of complexity and models the quantity of conditions in the rule Base. Finally, we show how the FRBCS tuned by our proposed algorithm can achieve a considerably better classification accuracy and complexity, expressed by the quantity of fuzzy rules and conditions in the RB compared with the FRBCS before the tuning process.

关键词： fuzzy rule-based classification system Type-2 fuzzy sets Multi-Objective Evolutionary Algorithm

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Hybrid fuzzy rule-based classification system for MOODLE LMS system

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JOURNAL OF INTERNET TECHNOLOGY 2021年第1期22卷 81-90页

作者： Zhao, Qun Lai, Shou-Chuan Wang, Jin-Long Wang, Li-Yu Ningbo Univ Coll Sci & Technol Ningbo Peoples R China Ming Chuan Univ Dept Informat & Telecommun Engn Taoyuan Taiwan Ming Chuan Univ Div Student Affairs Taoyuan Taiwan

Educational data are widely applied to predict students' academic performance in educational systems. Prior research mainly used students' past learning data to predict their future performance. However, these predicted results could not provide teachers with the opportunity to remediate the students in time. In order to achieve the effect of early warning, this study uses only the activity log of the first third of the semester to build models and prediction results. A hybrid classification decision mechanism is proposed to combine the results of different predictions based on the accumulated training cases to further improve the accuracy of prediction. The proposed system is then applied to discover students' learning outcomes in a C programming language course in the early stage of a semester according to the log files of the MOODLE LMS system. The results show that the transformation of learning activity data has a critical impact on prediction accuracy. Using cumulative training cases can significantly improve prediction accuracy. And the proposed hybrid fuzzy classification decision-making scheme, which combines data conversion with cumulative training cases, can produce higher prediction accuracy by using just one-third of a semester's learning activity data.

关键词： MOODLE Early warning fuzzy rule-based classification system

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Modified fuzzy rule-based classification system for Early Warning of Student Learning

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Journal of Educational Technology systems 2019年第3期48卷 385 - 406页

作者： Qun Zhao Jin-Long Wang Tsang-Long Pao Li-Yu Wang Department of Electronic Commerce at the College of Science and Technology Ningbo University People’s Republic of China Department of Information and Telecommunication Engineering Ming Chuan University Taipei City Taiwan Department of Computer Science and Engineering Tatung University Taipei City Taiwan

This study uses the log data from Moodle learning management system for predicting student learning performance in the first third of a semester. Since the quality of the data has great influence on the accuracy of machine learning, five major data transmission methods are used to enhance data quality of log file in the data preprocessing stage. Furthermore, the modified FRBCS-CHI (fuzzy rule-based classification system using Chi's technique) algorithm, based on the weighted consequence, is proposed to improve the prediction accuracy of classification. Thereafter, the confusion matrix with two dimensions is employed to illustrate the prediction results, such as false positives, false negatives, true positives, and true negatives, which are further used to produce the parameters of prediction performance, including the precision rate, the recall rate, and the F-measure. From the results of experiment, the proposed modified FRBCS-CHI method will have higher prediction accuracy than the original FRBCS-CHI method.

关键词： moodle learning warning prediction preprocessing fuzzy rule-based classification system

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Consensus via penalty functions for decision making in ensembles in fuzzy rule-based classification systems

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APPLIED SOFT COMPUTING 2018年 67卷 728-740页

作者： Elkano, Mikel Galar, Mikel Antonio Sanz, Jose Fernanda Schiavo, Paula Pereira, Sidnei, Jr. Pereira Dimuro, Gracaliz Borges, Eduardo N. Bustince, Humberto Univ Publ Navarra Dept Automot & Comp Campus Arrosadia Navarra 31006 Spain Univ Publ Navarra Inst Smart Cities Campus Arrosadia Navarra 31006 Spain Univ Fed Rio Grande Ctr Ciencias Computacionais Av Italia Km 08Campus Carreiros BR-96201900 Rio Grande Brazil

The aim of this paper is to propose a consensus method via penalty functions for decision making in ensembles of fuzzy rule-based classification systems (FRBCSs). For that, we first introduce a method based on overlap indices for building confidence and support measures, which are usually used to evaluate the degree of certainty or interest of a certain association rule. Those overlap indices (a generalizations of the Zadeh's consistency index between two fuzzy sets) are built using overlap functions, which are a special kind of non necessarily associative aggregation functions proposed for applications related to the overlap problem and/or when the associativity property is not demanded. Then, we introduce a new FRM for the FRBCS, considering different overlap indices, which generalizes the classical methods. By considering several overlap indices and aggregation functions, we generate fuzzy rule-based ensembles, providing different results. For the decision making related to the selection of the best class, we introduce a consensus method for classification, based on penalty functions. We also present theoretical results related to the developed methods. A detailed example of a generation of fuzzy rule-based ensembles based on the proposed approach, and the decision making by consensus via penalty functions, is presented. (C) 2017 Elsevier B.V. All rights reserved.

关键词： fuzzy rule-based classification system Aggregation function Penalty function Overlap function Overlap index Confidence and support measures

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Application of the Sugeno integral in fuzzy rule-based classification

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Wieczynski, Jonata Lucca, Giancarlo Borges, Eduardo Urio-Larrea, Asier Molina, Carlos Lopez Bustince, Humberto Dimuro, Gracaliz Univ Publ Navarra UPNA Dept Estadist Informat & Matemat Pamplona Navarra Spain Univ Catolica Pelotas UCPEL Mestrado Engn Elect & Computacao Pelotas RS Brazil Univ Fed Rio Grande FURG Ctr Ciencias Computacionais Rio Grande RS Brazil

fuzzy rule-based classification system (FRBCS) is a well-known technique to deal with classification problems. Recent studies have considered the usage of the Choquet integral and its generalizations (e.g.: C-T-integral, C-F-Integral and C-C-integral) to enhance the performance of such systems. Such fuzzy integrals were applied to the fuzzy Reasoning Method (FRM) to aggregate the fired fuzzy rules when classifying new data. However, the Sugeno integral, another well-known aggregation operator, obtained good results in other applications, such as brain-computer interfaces. These facts led to the present study, in which we consider the Sugeno integral in classification problems. That is, the Sugeno integral is applied in the FRM of a widely used FRBCS, and its performance is analyzed over 33 different datasets from the literature, also considering different fuzzy measures. To show the efficiency of this new approach, the results obtained are also compared with previous studies that involved the application of different aggregation functions. Finally, we perform a statistical analysis of the application.

关键词： Sugeno integral Choquet integral C-T-integral C-F-integral C-C-integral fuzzy measures fuzzy rule-based classification system

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Belief rule-based classification system: Extension of FRBCS in belief functions framework

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INFORMATION SCIENCES 2015年 309卷 26-49页

作者： Jiao, Lianmeng Pan, Quan Denoeux, Thierry Liang, Yan Feng, Xiaoxue Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Univ Technol Compiegne Heudiasyc UMR CNRS 7253 F-60205 Compiegne France Univ Technol Troyes ICD LM2S UMR CNRS 6279 F-10010 Troyes France

Among the computational intelligence techniques employed to solve classification problems, the fuzzy rule-based classification system (FRBCS) is a popular tool capable of building a linguistic model interpretable to users. However, it may face lack of accuracy in some complex applications, by the fact that the inflexibility of the concept of the linguistic variable imposes hard restrictions on the fuzzy rule structure. In this paper, we extend the fuzzy rule in FRBCS with a belief rule structure and develop a belief rule-based classification system (BRBCS) to address imprecise or incomplete information in complex classification problems. The two components of the proposed BRBCS, i.e., the belief rule base (BRB) and the belief reasoning method (BRM), are designed specifically by taking into account the pattern noise that existes in many real-world data sets. Four experiments based on benchmark data sets are carried out to evaluate the classification accuracy, robustness, interpretability and time complexity of the proposed method. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Pattern classification fuzzy rule-based classification system Belief rule-based classification system Belief functions theory Pattern noise

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Application of the Sugeno Integral in fuzzy rule-based classification 1

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11th Brazilian Conference on Intelligent systems (BRACIS)

作者： Wieczynski, Jonata Lucca, Giancarlo Borges, Eduardo Dimuro, Gracaliz Univ Publ Navarra Dept Estadist Informat & Matemat Pamplona Spain Univ Fed Rio Grande Ctr Ciecias Comp Rio Grande Brazil

ISBN: (数字)9783031216862

ISBN: (纸本)9783031216855;9783031216862

fuzzy rule-based classification system (FRBCS) is a well known technique to deal with classification problems. Recent studies have considered the usage of the Choquet integral and its generalizations to enhance the quality of such systems. Precisely, it was applied to the fuzzy Reasoning Method (FRM) to aggregate the fired fuzzy rules when classify new data. On the other side, the Sugeno integral, another well known aggregation operator, obtained good results when applied to brain-computer interfaces. Those facts led to the present study in which we consider the Sugeno integral in classification problems. That is, the Sugeno integral is applied in the FRM of a widely used FRBCS and its performance is analyzed over 33 different datasets from the literature. In order to show the efficiency of this new approach, the obtained results are also compared to past studies involving the application of different aggregation functions. Finally, we perform a statistical analysis of the application.

关键词： classification problem fuzzy rule-based classification system fuzzy reasoning method Sugeno integral Choquet integral

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EEG rhythm/channel selection for fuzzy rule-based alertness state characterization

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NEURAL COMPUTING & APPLICATIONS 2018年第7期30卷 2257-2267页

作者： Al-Ani, Ahmed Mesbah, Mostefa Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Sultan Qaboos Univ Dept Elect & Comp Engn Muscat Oman Univ Western Australia Sch Comp Sci & Software Engn Crawley WA 6009 Australia

The aim of the paper is to automatically select the optimal EEG rhythm/ channel combinations capable of classifying human alertness states. Four alertness states were considered, namely 'engaged', 'calm', 'drowsy' and 'asleep'. The features used in the automatic selection are the energies associated with the conventional rhythms, delta, theta, alpha beta and gamma, extracted from overlapping windows of the different EEG channels. The selection process consists of two stages. In the first stage, the optimal brain regions, represented by sets of EEG channels, are selected using a simple search technique based on support vector machine (SVM), extreme learning machine (ELM) and LDA classifiers. In the second stage, a fuzzy rule-based alertness classification system (FRBACS) is used to identify, from the previously selected EEG channels, the optimal features and their supports. The IF-THEN rules used in FRBACS are constructed using a novel differential evolution-based search algorithm particularly designed for this task. Each alertness state is represented by a set of IF-THEN rules whose antecedent parts contain EEG rhythm/channel combination. The selected spatio-frequency features were found to be good indicators of the different alertness states, as judged by the classification performance of the FRBACS that was found to be comparable to those of the SVM, ELM and LDA classifiers. Moreover, the proposed classification system has the advantage of revealing simple and easy to interpret decision rules associated with each of the alertness states.

关键词： Feature selection Alertness classification Drowsiness EEG fuzzy rule-based classification system Differential evolution

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