1
|
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]
|
2
|
Liu S, Wang B, Li H, Chen C, Wang Z. Continual portfolio selection in dynamic environments via incremental reinforcement learning. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01639-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
3
|
Liu J, Li T, Yuan Z, Huang W, Xie P, Huang Q. Symbolic aggregate approximation based data fusion model for dangerous driving behavior detection. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
4
|
Ferdaus MM, Zhou B, Yoon JW, Low KL, Pan J, Ghosh J, Wu M, Li X, Thean AVY, Senthilnath J. Significance of activation functions in developing an online classifier for semiconductor defect detection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108818] [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]
|
5
|
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]
|
6
|
Izadikhah M, Azadi M, Toloo M, Hussain FK. Sustainably resilient supply chains evaluation in public transport: A fuzzy chance-constrained two-stage DEA approach. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107879] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
7
|
Alves KSTR, Pestana de Aguiar E. A novel rule-based evolving Fuzzy System applied to the thermal modeling of power transformers. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107764] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
8
|
|
9
|
|
10
|
|
11
|
|
12
|
Ferdaus MM, Pratama M, Anavatti SG, Garratt MA. Online identification of a rotary wing Unmanned Aerial Vehicle from data streams. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2018.12.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
13
|
|
14
|
A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with Multilayered Probabilistic Neural Network classifier. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.04.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
15
|
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]
|
16
|
Rahmaninia M, Moradi P. OSFSMI: Online stream feature selection method based on mutual information. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.08.034] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
17
|
|
18
|
|
19
|
Pourpanah F, Lim CP, Hao Q. A reinforced fuzzy ARTMAP model for data classification. INT J MACH LEARN CYB 2018. [DOI: 10.1007/s13042-018-0843-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
20
|
Gupta S, Sanyal S. INNAMP: An incremental neural network architecture with monitor perceptron. AI COMMUN 2018. [DOI: 10.3233/aic-180767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Sharad Gupta
- Information Technology Department, Indian Institute of Information Technology, Allahabad, Deoghat, Jhalwa, Allahabad, India. E-mail:
| | - Sudip Sanyal
- Computer Science and Engineering, BML Munjal University, Gurgaon, Haryana, India. E-mail:
| |
Collapse
|
21
|
Learning of operator hand movements via least angle regression to be teached in a manipulator. EVOLVING SYSTEMS 2018. [DOI: 10.1007/s12530-018-9224-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
22
|
Bouillon M, Anquetil E. Online active supervision of an evolving classifier for customized-gesture-command learning. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.12.094] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
23
|
|
24
|
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]
|
25
|
|
26
|
|
27
|
Páramo-Carranza LA, Meda-Campaña JA, de Jesús Rubio J, Tapia-Herrera R, Curtidor-López AV, Grande-Meza A, Cázares-Ramírez I. Discrete-time Kalman filter for Takagi–Sugeno fuzzy models. EVOLVING SYSTEMS 2017. [DOI: 10.1007/s12530-017-9181-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
28
|
Pratama M, Zhang G, Er MJ, Anavatti S. An Incremental Type-2 Meta-Cognitive Extreme Learning Machine. IEEE TRANSACTIONS ON CYBERNETICS 2017; 47:339-353. [PMID: 26812744 DOI: 10.1109/tcyb.2016.2514537] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Existing extreme learning algorithm have not taken into account four issues: 1) complexity; 2) uncertainty; 3) concept drift; and 4) high dimensionality. A novel incremental type-2 meta-cognitive extreme learning machine (ELM) called evolving type-2 ELM (eT2ELM) is proposed to cope with the four issues in this paper. The eT2ELM presents three main pillars of human meta-cognition: 1) what-to-learn; 2) how-to-learn; and 3) when-to-learn. The what-to-learn component selects important training samples for model updates by virtue of the online certainty-based active learning method, which renders eT2ELM as a semi-supervised classifier. The how-to-learn element develops a synergy between extreme learning theory and the evolving concept, whereby the hidden nodes can be generated and pruned automatically from data streams with no tuning of hidden nodes. The when-to-learn constituent makes use of the standard sample reserved strategy. A generalized interval type-2 fuzzy neural network is also put forward as a cognitive component, in which a hidden node is built upon the interval type-2 multivariate Gaussian function while exploiting a subset of Chebyshev series in the output node. The efficacy of the proposed eT2ELM is numerically validated in 12 data streams containing various concept drifts. The numerical results are confirmed by thorough statistical tests, where the eT2ELM demonstrates the most encouraging numerical results in delivering reliable prediction, while sustaining low complexity.
Collapse
|
29
|
Wang J, Huang JZ, Wu D, Guo J, Lan Y. An incremental model on search engine query recommendation. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.09.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
30
|
Zheng S, Xu J, Zhou P, Bao H, Qi Z, Xu B. A neural network framework for relation extraction: Learning entity semantic and relation pattern. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.09.019] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
31
|
|