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ORCIDDaniel F. Leite
Person information
- affiliation: Paderborn University, Department of Computer Science, Germany
- affiliation (2022 - 2023): Adolfo Ibáñez University, Santiago, Chile
- affiliation (2014 - 2022): Federal University of Lavras, Department of Engineering, MG, Brazil
Other persons with the same name
- Daniel Leite — disambiguation page
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2020 – today
- 2024
[c26]Daniel F. Leite
, Alisson Silva, Gabriella Casalino, Arnab Sharma, Danielle Fortunato, Axel-Cyrille Ngonga Ngomo:
EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams. EAIS 2024: 1-10- [c25]Daniel F. Leite, Arnab Sharma, Caglar Demir, Axel-Cyrille Ngonga Ngomo:
Interpretability Index Based on Balanced Volumes for Transparent Models and Agnostic Explainers. FUZZ 2024: 1-10 - [c24]Arnab Sharma, Daniel F. Leite, Caglar Demir, Axel-Cyrille Ngonga Ngomo:
Trading-Off Interpretability and Accuracy in Medical Applications: A Study Toward Optimal Explainability of Hoeffding Trees. FUZZ 2024: 1-10 - [i8]Daniel Leite, Alisson Silva, Gabriella Casalino, Arnab Sharma, Danielle Fortunato, Axel-Cyrille Ngonga Ngomo:
EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams. CoRR abs/2402.17792 (2024) - 2023
- [j19]Gabriella Casalino, Giovanna Castellano, Olgierd Hryniewicz, Daniel Leite, Karol R. Opara, Weronika Radziszewska, Katarzyna Kaczmarek-Majer:
Semi-supervised vs. supervised learning for mental health monitoring: A case study on bipolar disorder. Int. J. Appl. Math. Comput. Sci. 33(3) (2023) - [j18]Plamen Angelov, Dimitar P. Filev, Nikola K. Kasabov, Daniel Leite, Maharadhika Pratama, Susanne Saminger-Platz, Erich-Peter Klement:
Obituary Edwin Lughofer (1972-2023). Evol. Syst. 14(5): 747-748 (2023) - [j17]Daniel F. Leite, Igor Skrjanc, Saso Blazic, Andrej Zdesar, Fernando A. C. Gomide:
Interval incremental learning of interval data streams and application to vehicle tracking. Inf. Sci. 630: 1-22 (2023) - [c23]Daniel F. Leite, Fernando A. C. Gomide:
Data Driven Fuzzy and Neural Dynamic Systems Modeling. ICMLC 2023: 321-325 - [e2]Gabriella Casalino, Giovanna Castellano, Katarzyna Kaczmarek-Majer, Daniel Leite:
Proceedings of the First Workshop on Online Learning from Uncertain Data Streams (OLUD 2022) co-located with IEEE World Congress on Computational Intelligence (WCCI 2022), Padova, Italy, July 18, 2022. CEUR Workshop Proceedings 3380, CEUR-WS.org 2023 [contents] - 2022
- [j16]Tatiane Carvalho Alvarenga, Renato Ribeiro de Lima, Sérgio Domingos Simão, Luiz Carlos Brandão Júnior, Júlio Sílvio de Sousa Bueno Filho, Renata Ribeiro Alvarenga, Paulo Borges Rodrigues, Daniel Furtado Leite:
Ensemble of hybrid Bayesian networks for predicting the AMEn of broiler feedstuffs. Comput. Electron. Agric. 198: 107067 (2022) - [j15]Leticia Decker, Daniel F. Leite, Francesco Minarini, Simone Rossi Tisbeni, Daniele Bonacorsi:
Unsupervised Learning and Online Anomaly Detection: An On-Condition Log-Based Maintenance System. Int. J. Embed. Real Time Commun. Syst. 13(1): 1-16 (2022) - [c22]Leticia Decker, Daniel F. Leite, Daniele Bonacorsi:
Explainable Log Parsing and Online Interval Granular Classification from Streams of Words. FUZZ-IEEE 2022: 1-8 - [c21]Daniel F. Leite, Fernando A. C. Gomide, Ronald R. Yager:
Data Driven Fuzzy Modeling Using Level Sets. FUZZ-IEEE 2022: 1-5 - [c20]Katarzyna Kaczmarek-Majer, Gabriella Casalino, Giovanna Castellano, Daniel F. Leite, Olgierd Hryniewicz:
Fuzzy Linguistic Summaries for Explaining Online Semi-Supervised Learning. IS 2022: 1-8 - [e1]Plamen Angelov, George A. Papadopoulos, Giovanna Castellano, José A. Iglesias, Gabriella Casalino, Edwin Lughofer, Daniel Leite:
IEEE International Conference on Evolving and Adaptive Intelligent System, EAIS 2022, Larnaca, Cyprus, May 25-26, 2022. IEEE 2022, ISBN 978-1-6654-3706-6 [contents] - 2021
- [j14]Charles Aguiar, Daniel F. Leite, Daniel Pereira, Goran Andonovski, Igor Skrjanc:
Nonlinear modeling and robust LMI fuzzy control of overhead crane systems. J. Frankl. Inst. 358(2): 1376-1402 (2021) - [c19]Daniel F. Leite, Volnei Frigeri Jr., Rodrigo Medeiros:
Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games. LA-CCI 2021: 1-6 - [i7]Daniel F. Leite, Pedro Henrique Silva Coutinho, Iury Bessa, Murilo C. O. Camargos Filho, Luiz Cordovil Junior, Reinaldo M. Palhares:
Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model. CoRR abs/2102.09503 (2021) - [i6]Murilo C. O. Camargos, Iury Bessa, Luiz A. Q. Cordovil Junior, Pedro Henrique Silva Coutinho, Daniel Furtado Leite, Reinaldo Martinez Palhares:
Evolving Fuzzy System Applied to Battery Charge Capacity Prediction for Fault Prognostics. CoRR abs/2102.09521 (2021) - [i5]Daniel F. Leite, Volnei Frigeri Jr., Rodrigo Medeiros:
Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games. CoRR abs/2103.03488 (2021) - 2020
- [j13]Fabricio P. Lucas, Pyramo Costa, Rose M. S. Batalha, Daniel F. Leite, Igor Skrjanc:
Fault detection in smart grids with time-varying distributed generation using wavelet energy and evolving neural networks. Evol. Syst. 11(2): 165-180 (2020) - [j12]Daniel F. Leite, Igor Skrjanc, Fernando A. C. Gomide:
An overview on evolving systems and learning from stream data. Evol. Syst. 11(2): 181-198 (2020) - [j11]Sergio Silva, Pyramo Costa, Marcio Santana, Daniel F. Leite:
Evolving neuro-fuzzy network for real-time high impedance fault detection and classification. Neural Comput. Appl. 32(12): 7597-7610 (2020) - [j10]Daniel F. Leite, Goran Andonovski, Igor Skrjanc, Fernando A. C. Gomide:
Optimal Rule-Based Granular Systems From Data Streams. IEEE Trans. Fuzzy Syst. 28(3): 583-596 (2020) - [j9]Cristiano Garcia, Daniel F. Leite, Igor Skrjanc:
Incremental Missing-Data Imputation for Evolving Fuzzy Granular Prediction. IEEE Trans. Fuzzy Syst. 28(10): 2348-2362 (2020) - [c18]Charles Aguiar, Daniel F. Leite:
Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering. EAIS 2020: 1-8 - [c17]Leticia Decker, Daniel F. Leite, Fabio Viola, Daniele Bonacorsi:
Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers. EAIS 2020: 1-8 - [c16]Leticia Decker, Daniel F. Leite, Luca Giommi, Daniele Bonacorsi:
Real-Time Anomaly Detection in Data Centers for Log-based Predictive Maintenance using an Evolving Fuzzy-Rule-Based Approach. FUZZ-IEEE 2020: 1-8 - [c15]Daniel F. Leite, Leticia Decker, Marcio Santana, Paulo Vitor de Campos Souza:
EGFC: Evolving Gaussian Fuzzy Classifier from Never-Ending Semi-Supervised Data Streams - With Application to Power Quality Disturbance Detection and Classification. FUZZ-IEEE 2020: 1-9 - [i4]Charles Aguiar, Daniel F. Leite:
Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering. CoRR abs/2003.12381 (2020) - [i3]Daniel F. Leite, Leticia Decker, Marcio Santana, Paulo Vitor de Campos Souza:
EGFC: Evolving Gaussian Fuzzy Classifier from Never-Ending Semi-Supervised Data Streams - With Application to Power Quality Disturbance Detection and Classification. CoRR abs/2004.09986 (2020) - [i2]Leticia Decker, Daniel F. Leite, Luca Giommi, Daniele Bonacorsi:
Real-Time Anomaly Detection in Data Centers for Log-based Predictive Maintenance using an Evolving Fuzzy-Rule-Based Approach. CoRR abs/2004.13527 (2020) - [i1]Leticia Decker, Daniel F. Leite, Fabio Viola, Daniele Bonacorsi:
Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers. CoRR abs/2005.04156 (2020)
2010 – 2019
- 2019
- [j8]Igor Skrjanc, José Antonio Iglesias, Araceli Sanchis, Daniel F. Leite, Edwin Lughofer, Fernando A. C. Gomide:
Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey. Inf. Sci. 490: 344-368 (2019) - [j7]Daniel F. Leite, Igor Skrjanc:
Ensemble of evolving optimal granular experts, OWA aggregation, and time series prediction. Inf. Sci. 504: 95-112 (2019) - [j6]Cristiano Garcia, Ahmed Esmin, Daniel F. Leite, Igor Skrjanc:
Evolvable fuzzy systems from data streams with missing values: With application to temporal pattern recognition and cryptocurrency prediction. Pattern Recognit. Lett. 128: 278-282 (2019) - [c14]Daniel F. Leite, Charles Aguiar, Daniel Pereira, Gustavo Souza, Igor Skrjanc:
Nonlinear Fuzzy State-Space Modeling and LMI Fuzzy Control of Overhead Cranes. FUZZ-IEEE 2019: 1-6 - [c13]Daniel F. Leite, Fernando A. C. Gomide, Igor Skrjanc:
Multiobjective Optimization of Fully Autonomous Evolving Fuzzy Granular Models. FUZZ-IEEE 2019: 1-7 - 2018
- [j5]Eduardo A. Soares, Pyramo Costa, Bruno Costa, Daniel F. Leite:
Ensemble of evolving data clouds and fuzzy models for weather time series prediction. Appl. Soft Comput. 64: 445-453 (2018) - [j4]Vania C. Mota, Flavio A. Damasceno, Daniel F. Leite:
Fuzzy clustering and fuzzy validity measures for knowledge discovery and decision making in agricultural engineering. Comput. Electron. Agric. 150: 118-124 (2018) - [c12]Eduardo A. Soares, Heloisa A. Camargo, Suzana J. Camargo, Daniel F. Leite:
Incremental Gaussian Granular Fuzzy Modeling Applied to Hurricane Track Forecasting. FUZZ-IEEE 2018: 1-8 - [c11]Fabricio P. Lucas, Pyramo Costa, Rose M. S. Batalha, Daniel F. Leite:
High Impedance Fault Detection in Time-Varying Distributed Generation Systems Using Adaptive Neural Networks. IJCNN 2018: 1-8 - 2017
- [c10]Vania C. Mota, Flavio A. Damasceno, Eduardo A. Soares, Daniel F. Leite:
Fuzzy clustering methods applied to the evaluation of compost bedded pack barns. FUZZ-IEEE 2017: 1-6 - [c9]Eduardo A. Soares, Vania C. Mota, Ricardo Poucas, Daniel F. Leite:
Cloud-based evolving intelligent method for weather time series prediction. FUZZ-IEEE 2017: 1-6 - 2016
- [c8]Daniel F. Leite, Marcio Santana, Ana Borges, Fernando A. C. Gomide:
Fuzzy Granular Neural Network for incremental modeling of nonlinear chaotic systems. FUZZ-IEEE 2016: 64-71 - 2015
- [j3]Daniel F. Leite, Reinaldo M. Palhares, Víctor C. S. Campos, Fernando A. C. Gomide:
Evolving Granular Fuzzy Model-Based Control of Nonlinear Dynamic Systems. IEEE Trans. Fuzzy Syst. 23(4): 923-938 (2015) - [c7]Lourenco Bueno, Pyramo Costa, Israel Mendes, Enderson Cruz, Daniel F. Leite:
Evolving ensemble of fuzzy models for multivariate time series prediction. FUZZ-IEEE 2015: 1-6 - [p2]Daniel F. Leite, Fernando A. C. Gomide:
Incremental Granular Fuzzy Modeling Using Imprecise Data Streams. Fifty Years of Fuzzy Logic and its Applications 2015: 107-124 - 2013
- [j2]Daniel F. Leite, Pyramo Costa Jr., Fernando A. C. Gomide:
Evolving granular neural networks from fuzzy data streams. Neural Networks 38: 1-16 (2013) - 2012
- [b1]Daniel Furtado Leite:
Sistemas granulares evolutivos. University of Campinas, Brazil, 2012 - [j1]Daniel F. Leite, Rosangela Ballini, Pyramo Costa Jr., Fernando A. C. Gomide:
Evolving fuzzy granular modeling from nonstationary fuzzy data streams. Evol. Syst. 3(2): 65-79 (2012) - [c6]André Paim Lemos, Daniel F. Leite, Leandro Maciel, Rosangela Ballini, Walmir M. Caminhas, Fernando A. C. Gomide:
Evolving fuzzy linear regression tree approach for forecasting sales volume of petroleum products. FUZZ-IEEE 2012: 1-8 - [c5]Daniel F. Leite, Pyramo Costa Jr., Fernando A. C. Gomide:
Evolving granular neural network for fuzzy time series forecasting. IJCNN 2012: 1-8 - [p1]Daniel F. Leite, Fernando A. C. Gomide:
Evolving Linguistic Fuzzy Models from Data Streams. Combining Experimentation and Theory 2012: 209-223 - 2011
- [c4]Daniel F. Leite, Fernando A. C. Gomide, Rosangela Ballini, Pyramo Costa Jr.:
Fuzzy granular evolving modeling for time series prediction. FUZZ-IEEE 2011: 2794-2801 - 2010
- [c3]Daniel F. Leite, Pyramo Costa Jr., Fernando A. C. Gomide:
Evolving granular neural network for semi-supervised data stream classification. IJCNN 2010: 1-8 - [c2]Daniel F. Leite, Pyramo Costa Jr., Fernando A. C. Gomide:
Granular Approach for Evolving System Modeling. IPMU 2010: 340-349
2000 – 2009
- 2009
- [c1]Daniel F. Leite, Pyramo Costa Jr., Fernando A. C. Gomide:
Evolving granular classification neural networks. IJCNN 2009: 1736-1743
Coauthor Index
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