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For:
Antonelli M
, Ducange P, Marcelloni F.
An efficient multi-objective evolutionary fuzzy system for regression problems.
Int J Approx Reason
2013. [DOI:
10.1016/j.ijar.2013.06.005
]
[
Citation(s) in
RCA
: 15
]
[
Impact Index Per Article: 1.3
]
[
Reference Citation Analysis
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[Indexed: 10/26/2022]
Number
Cited by Other Article(s)
1
Aghaeipoor F
, Javidi MM. MOKBL+MOMs: An interpretable multi-objective evolutionary fuzzy system for learning high-dimensional regression data.
Inf Sci (N Y)
2019. [DOI:
10.1016/j.ins.2019.04.035
]
[
Citation(s) in
RCA
: 10
]
[
Impact Index Per Article: 1.7
]
[
Reference Citation Analysis
]
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[Indexed: 10/26/2022]
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2
Gámez JC
, García D, González A, Pérez R. An approximation to solve regression problems with a genetic fuzzy rule ordinal algorithm.
Appl Soft Comput
2019. [DOI:
10.1016/j.asoc.2019.02.012
]
[
Citation(s) in
RCA
: 2
]
[
Impact Index Per Article: 0.3
]
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Reference Citation Analysis
]
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[Indexed: 10/27/2022]
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3
Evolutionary Design of Linguistic Fuzzy Regression Systems with Adaptive Defuzzification in Big Data Environments.
Cognit Comput
2019. [DOI:
10.1007/s12559-019-09632-4
]
[
Citation(s) in
RCA
: 10
]
[
Impact Index Per Article: 1.7
]
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[Indexed: 11/26/2022]
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4
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: 1.4
]
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[Indexed: 11/16/2022]
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5
A participatory search algorithm.
EVOLUTIONARY INTELLIGENCE
2017. [DOI:
10.1007/s12065-016-0151-4
]
[
Citation(s) in
RCA
: 1
]
[
Impact Index Per Article: 0.1
]
[
Reference Citation Analysis
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[Indexed: 10/19/2022]
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6
Rodríguez-Fdez I
, Mucientes M, Bugarín A. S-FRULER: Scalable fuzzy rule learning through evolution for regression.
Knowl Based Syst
2016. [DOI:
10.1016/j.knosys.2016.07.034
]
[
Citation(s) in
RCA
: 11
]
[
Impact Index Per Article: 1.2
]
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Reference Citation Analysis
]
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[Indexed: 10/21/2022]
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7
Rodríguez-Fdez I
, Mucientes M, Bugarín A. FRULER: Fuzzy Rule Learning through Evolution for Regression.
Inf Sci (N Y)
2016. [DOI:
10.1016/j.ins.2016.03.012
]
[
Citation(s) in
RCA
: 23
]
[
Impact Index Per Article: 2.6
]
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Reference Citation Analysis
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[Indexed: 10/22/2022]
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8
GPFIS-CLASS: A Genetic Fuzzy System based on Genetic Programming for classification problems.
Appl Soft Comput
2015. [DOI:
10.1016/j.asoc.2015.08.055
]
[
Citation(s) in
RCA
: 17
]
[
Impact Index Per Article: 1.7
]
[
Reference Citation Analysis
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[Indexed: 11/19/2022]
Abstract
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9
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: 1.1
]
[
Reference Citation Analysis
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[Indexed: 11/17/2022]
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10
Fazzolari M
, Alcalá R, Herrera F. A multi-objective evolutionary method for learning granularities based on fuzzy discretization to improve the accuracy-complexity trade-off of fuzzy rule-based classification systems: D-MOFARC algorithm.
Appl Soft Comput
2014. [DOI:
10.1016/j.asoc.2014.07.019
]
[
Citation(s) in
RCA
: 44
]
[
Impact Index Per Article: 4.0
]
[
Reference Citation Analysis
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[Indexed: 11/26/2022]
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11
Acampora G
, Pedrycz W, Vasilakos AV. Efficient modeling of MIMO systems through Timed Automata based Neuro-Fuzzy Inference Engine.
Int J Approx Reason
2014. [DOI:
10.1016/j.ijar.2014.02.003
]
[
Citation(s) in
RCA
: 7
]
[
Impact Index Per Article: 0.6
]
[
Reference Citation Analysis
]
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[Indexed: 11/28/2022]
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