51
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Kronberger G, Kommenda M, Lughofer E, Saminger-Platz S, Promberger A, Nickel F, Winkler S, Affenzeller M. Using robust generalized fuzzy modeling and enhanced symbolic regression to model tribological systems. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.04.048] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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52
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Cózar J, delaOssa L, Gámez JA. Learning compact zero-order TSK fuzzy rule-based systems for high-dimensional problems using an Apriori + local search approach. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2017.12.026] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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53
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54
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Likelihood-fuzzy analysis: From data, through statistics, to interpretable fuzzy classifiers. Int J Approx Reason 2018. [DOI: 10.1016/j.ijar.2017.10.022] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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55
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Hajek P. Predicting corporate investment/non-investment grade by using interval-valued fuzzy rule-based systems—A cross-region analysis. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.10.037] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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56
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Cocaña-Fernández A, Ranilla J, Gil-Pita R, Sánchez L. Multicriteria Design of Cost-Conscious Fuzzy Rule-Based Classifiers. INT J UNCERTAIN FUZZ 2017. [DOI: 10.1142/s0218488517400074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Many real-world classification systems must comply with a series of inherent restrictions to the problem at hand such as response times, power consumptions or computational costs. This poses a fundamental limitation to traditional performance-driven classifiers and learning algorithms by restraining their applicability in cost-sensitive scenarios. Because of this, fuzzy systems are leveraged to learn cost-conscious multi-stage classifiers through multiobjective optimization to find a set of optimal tradeoffs between accuracy and any related cost. This approach allows find a suitable balance between all objectives regardless of the scenario. Experimental evaluations were done for Sound Environment Classification in modern battery-powered hearing aids by jointly optimising classification accuracy and computational costs.
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Affiliation(s)
| | - José Ranilla
- Departamento de Informática, Universidad de Oviedo, 33204 Gijón, Asturias, Spain
| | - Roberto Gil-Pita
- Departmento de Teoría de la Señal y Comunicaciones, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain
| | - Luciano Sánchez
- Departamento de Informática, Universidad de Oviedo, 33204 Gijón, Asturias, Spain
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57
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Ferranti A, Marcelloni F, Segatori A, Antonelli M, Ducange P. A distributed approach to multi-objective evolutionary generation of fuzzy rule-based classifiers from big data. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2017.06.039] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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58
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A multi-objective evolutionary fuzzy system to obtain a broad and accurate set of solutions in intrusion detection systems. Soft comput 2017. [DOI: 10.1007/s00500-017-2856-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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59
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Behadada O, Trovati M, Kontonatsios G, Korkontzelos Y. A Multinomial Logistic Regression Approach for Arrhythmia Detection. INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2017. [DOI: 10.4018/ijdst.2017100102] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Cardiovascular diseases are the leading causes on mortality in the world. Consequently, tools and methods providing useful and applicable insights into their assessment play a crucial role in the prediction and managements of specific heart conditions. In this article, we introduce a method based on multi-class Logistic Regression as a classifier to provide a powerful and accurate insight into cardiac arrhythmia, which is one of the predictors of serious vascular diseases. As suggested by our evaluation, this provides a robust, scalable, and accurate system, which can successfully tackle the challenges posed by the utilisation of big data in the medical sector.
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60
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Rey M, Galende M, Fuente M, Sainz-Palmero G. Multi-objective based Fuzzy Rule Based Systems (FRBSs) for trade-off improvement in accuracy and interpretability: A rule relevance point of view. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2016.12.028] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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61
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Yankovskaya AE, Gorbunov IV, Hodashinsky IA. Tradeoff search methods between interpretability and accuracy of the identification fuzzy systems based on rules. PATTERN RECOGNITION AND IMAGE ANALYSIS 2017. [DOI: 10.1134/s1054661817020134] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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62
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63
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Ahmed MM, Isa NAM. Knowledge base to fuzzy information granule: A review from the interpretability-accuracy perspective. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2016.12.055] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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64
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Big data analytics by automated generation of fuzzy rules for Network Forensics Readiness. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2016.10.029] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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65
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Cardoso M, Loula A, Pires MG. Automated Fuzzy System Based on Feature Extraction and Selection for Opinion Classification Across Different Domains. INT J UNCERTAIN FUZZ 2017. [DOI: 10.1142/s0218488516400122] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Opinions have a relevant inuence on people’s behavior. The Internet and the Web have made it possible for people to share their opinions and for other people and organizations find out more about opinions and experiences from individuals. Still, opinions involve sentiments that are vague and imprecise textual descriptions. Hence, due to the nature of the data, Fuzzy Logic can be a promising approach. This paper proposes a method to automatically build a fuzzy system, based on features extracted and selected from documents, to perform classification of sentiment in opinions across different domains. Almost 60 features were extracted from documents and multiple feature selection algorithms were applied. Over the selected features, the Wang-Mendel (WM) method was used to generate fuzzy rules and classify documents. Variations on fuzzy set modeling, on the use of weights in the rules and on fuzzy inference mechanisms were considered. The classifier fuzzy system based achieved 71,25% of accuracy in a 10-fold cross-validation, comparable to a SVM classifier.
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Affiliation(s)
- Matheus Cardoso
- State University of Feira de Santana (UEFS) and Federal University of Bahia (UFBA), Salvador, Brazil
| | - Angelo Loula
- State University of Feira de Santana (UEFS), Feira de Santana, Brazil
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66
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Ibarguren I, Lasarguren A, Pérez JM, Muguerza J, Gurrutxaga I, Arbelaitz O. BFPART: Best-First PART. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2016.07.023] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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67
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Tashayo B, Alimohammadi A. Modeling urban air pollution with optimized hierarchical fuzzy inference system. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2016; 23:19417-19431. [PMID: 27378222 DOI: 10.1007/s11356-016-7059-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2016] [Accepted: 06/07/2016] [Indexed: 06/06/2023]
Abstract
Environmental exposure assessments (EEA) and epidemiological studies require urban air pollution models with appropriate spatial and temporal resolutions. Uncertain available data and inflexible models can limit air pollution modeling techniques, particularly in under developing countries. This paper develops a hierarchical fuzzy inference system (HFIS) to model air pollution under different land use, transportation, and meteorological conditions. To improve performance, the system treats the issue as a large-scale and high-dimensional problem and develops the proposed model using a three-step approach. In the first step, a geospatial information system (GIS) and probabilistic methods are used to preprocess the data. In the second step, a hierarchical structure is generated based on the problem. In the third step, the accuracy and complexity of the model are simultaneously optimized with a multiple objective particle swarm optimization (MOPSO) algorithm. We examine the capabilities of the proposed model for predicting daily and annual mean PM2.5 and NO2 and compare the accuracy of the results with representative models from existing literature. The benefits provided by the model features, including probabilistic preprocessing, multi-objective optimization, and hierarchical structure, are precisely evaluated by comparing five different consecutive models in terms of accuracy and complexity criteria. Fivefold cross validation is used to assess the performance of the generated models. The respective average RMSEs and coefficients of determination (R (2)) for the test datasets using proposed model are as follows: daily PM2.5 = (8.13, 0.78), annual mean PM2.5 = (4.96, 0.80), daily NO2 = (5.63, 0.79), and annual mean NO2 = (2.89, 0.83). The obtained results demonstrate that the developed hierarchical fuzzy inference system can be utilized for modeling air pollution in EEA and epidemiological studies.
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Affiliation(s)
- Behnam Tashayo
- Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, Khajeh Nasir Toosi University of Technology, Vali-Asr Street, Mirdamad Cross, Tehran, Iran.
| | - Abbas Alimohammadi
- Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, Khajeh Nasir Toosi University of Technology, Vali-Asr Street, Mirdamad Cross, Tehran, Iran
- Center of Excellence in Geospatial Information Technology, Faculty of Geodesy and Geomatics Engineering, Khajeh Nasir Toosi University of Technology, Tehran, Iran
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68
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Wang Y, Dai Y, Chen YW, Pedrycz W. An Interpretability-Accuracy Tradeoff in Learning Parameters of Intuitionistic Fuzzy Rule-Based Systems. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2016. [DOI: 10.20965/jaciii.2016.p0773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Parameter learning of Intuitionistic Fuzzy Rule-Based Systems (IFRBSs) is discussed and applied to medical diagnosis with intent of establishing a sound tradeoff between interpretability and accuracy. This study aims to improve the accuracy of IFRBSs without sacrificing its interpretability. This paper proposes an Objective Programming Method with an Interpretability-Accuracy tradeoff (OPMIA) to learn the parameters of IFRBSs by tuning the types of membership and non-membership functions and by adjusting adaptive factors and rule weights. The proposed method has been validated in the context of a medical diagnosis problem and a well-known publicly available auto-mpg data set. Furthermore, the proposed method is compared to Objective Programming Method not considering the interpretability (OPMNI) and Objective Programming Method based on Similarity Measure (OPMSM). The OPMIA helps achieve a sound a tradeoff between accuracy and interpretability and demonstrates its advantages over the other two methods.
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69
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Jamalabadi H, Nasrollahi H, Alizadeh S, Nadjar Araabi B, Nili Ahamadabadi M. Competitive interaction reasoning: A bio-inspired reasoning method for fuzzy rule based classification systems. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2016.02.052] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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70
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Ahmad SSS, Pedrycz W. The development of granular rule-based systems: a study in structural model compression. GRANULAR COMPUTING 2016. [DOI: 10.1007/s41066-016-0022-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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71
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Fernández A, del Río S, Bawakid A, Herrera F. Fuzzy rule based classification systems for big data with MapReduce: granularity analysis. ADV DATA ANAL CLASSI 2016. [DOI: 10.1007/s11634-016-0260-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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72
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Fernández A, Elkano M, Galar M, Sanz JA, Alshomrani S, Bustince H, Herrera F. Enhancing evolutionary fuzzy systems for multi-class problems: Distance-based relative competence weighting with truncated confidences (DRCW-TC). Int J Approx Reason 2016. [DOI: 10.1016/j.ijar.2016.02.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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73
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Cpałka K, Zalasiński M, Rutkowski L. A new algorithm for identity verification based on the analysis of a handwritten dynamic signature. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.02.017] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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74
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75
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Mathew JK, Mathew S. On strong extremal problems in fuzzy graphs. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2016. [DOI: 10.3233/ifs-151711] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jill K. Mathew
- Department of Mathematics, Federal Institute of Science and Technology, Angamaly, Kerala, India
| | - Sunil Mathew
- Department of Mathematics, National Institute of Technology, Calicut, India
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76
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Bala R, Ratnoo S. A Genetic Algorithm Approach for Discovering Tuned Fuzzy Classification Rules with Intra- and Inter-Class Exceptions. JOURNAL OF INTELLIGENT SYSTEMS 2016. [DOI: 10.1515/jisys-2015-0136] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
AbstractFuzzy rule-based systems (FRBSs) are proficient in dealing with cognitive uncertainties like vagueness and ambiguity imperative to real-world decision-making situations. Fuzzy classification rules (FCRs) based on fuzzy logic provide a framework for a flexible human-like reasoning involving linguistic variables. Appropriate membership functions (MFs) and suitable number of linguistic terms – according to actual distribution of data – are useful to strengthen the knowledge base (rule base [RB]+ data base [DB]) of FRBSs. An RB is expected to be accurate and interpretable, and a DB must contain appropriate fuzzy constructs (type of MFs, number of linguistic terms, and positioning of parameters of MFs) for the success of any FRBS. Moreover, it would be fascinating to know how a system behaves in some rare/exceptional circumstances and what action ought to be taken in situations where generalized rules cease to work. In this article, we propose a three-phased approach for discovery of FCRs augmented with intra- and inter-class exceptions. A pre-processing algorithm is suggested to tune DB in terms of the MFs and number of linguistic terms for each attribute of a data set in the first phase. The second phase discovers FCRs employing a genetic algorithm approach. Subsequently, intra- and inter-class exceptions are incorporated in the rules in the third phase. The proposed approach is illustrated on an example data set and further validated on six UCI machine learning repository data sets. The results show that the approach has been able to discover more accurate, interpretable, and interesting rules. The rules with intra-class exceptions tell us about the unique objects of a category, and rules with inter-class exceptions enable us to take a right decision in the exceptional circumstances.
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Affiliation(s)
- Renu Bala
- 1Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar-125001, India
| | - Saroj Ratnoo
- 1Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar-125001, India
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77
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A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2015.11.037] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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78
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79
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Espin-Andrade RA, Gonzalez E, Pedrycz W, Fernandez E. An Interpretable Logical Theory: The case of Compensatory Fuzzy Logic. INT J COMPUT INT SYS 2016. [DOI: 10.1080/18756891.2016.1204111] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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80
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Fernández A, Carmona CJ, del Jesus MJ, Herrera F. A View on Fuzzy Systems for Big Data: Progress and Opportunities. INT J COMPUT INT SYS 2016. [DOI: 10.1080/18756891.2016.1180820] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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81
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Rudziński F. A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2015.09.038] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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82
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Multi-objective evolutionary design of granular rule-based classifiers. GRANULAR COMPUTING 2015. [DOI: 10.1007/s41066-015-0004-z] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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83
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An interpretability improvement for fuzzy rule bases obtained by the iterative rule learning approach. Int J Approx Reason 2015. [DOI: 10.1016/j.ijar.2015.09.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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84
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Mansoori EG, Shafiee KS. On fuzzy feature selection in designing fuzzy classifiers for high-dimensional data. EVOLVING SYSTEMS 2015. [DOI: 10.1007/s12530-015-9142-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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85
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A discussion on interpretability of linguistic rule based systems and its application to solve regression problems. Knowl Based Syst 2015. [DOI: 10.1016/j.knosys.2015.08.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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86
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87
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88
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Jiao L, Pan Q, Denœux T, Liang Y, Feng X. Belief rule-based classification system: Extension of FRBCS in belief functions framework. Inf Sci (N Y) 2015. [DOI: 10.1016/j.ins.2015.03.005 10.1016/j.ins.2015.03.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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89
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Fernández A, López V, del Jesus MJ, Herrera F. Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges. Knowl Based Syst 2015. [DOI: 10.1016/j.knosys.2015.01.013] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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90
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91
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Rules discovery in fuzzy classifier systems with PSO for scheduling in grid computational infrastructures. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2014.11.064] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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92
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Performance analysis of fuzzy rule based classification system for transient identification in nuclear power plant. ANN NUCL ENERGY 2015. [DOI: 10.1016/j.anucene.2014.09.039] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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93
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Antonelli M, Ducange P, Marcelloni F. An experimental study on evolutionary fuzzy classifiers designed for managing imbalanced datasets. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2014.04.070] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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94
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Rini DP, Shamsuddin SM, Yuhaniz SS. Particle swarm optimization for ANFIS interpretability and accuracy. Soft comput 2014. [DOI: 10.1007/s00500-014-1498-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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95
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Antonelli M, Ducange P, Marcelloni F. A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2014.06.014] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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96
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Comparison and design of interpretable linguistic vs. scatter FRBSs: Gm3m generalization and new rule meaning index for global assessment and local pseudo-linguistic representation. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2014.05.023] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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97
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Modeling of a semantics core of linguistic terms based on an extension of hedge algebra semantics and its application. Knowl Based Syst 2014. [DOI: 10.1016/j.knosys.2014.04.047] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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98
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Fuzzy partitioning for clinical DSSs using statistical information transformed into possibility-based knowledge. Knowl Based Syst 2014. [DOI: 10.1016/j.knosys.2014.06.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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99
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Gacto M, Galende M, Alcalá R, Herrera F. METSK-HDe: A multiobjective evolutionary algorithm to learn accurate TSK-fuzzy systems in high-dimensional and large-scale regression problems. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2014.02.047] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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100
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Cpałka K, Łapa K, Przybył A, Zalasiński M. A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.12.031] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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