.png){kind=icon}
Return
Fuzzy granular recurrence plot and quantification analysis: A novel method for classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2023.109456.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recently, recurrence plot (RP) and its quantification techniques have become an important research tool in nonlinear analysis. In the existing researches, an RP is directly established on a time series ignoring the influence of noise on data, which will affect our judgement on the dynamic properties of a system. To tackle the problem there, this paper proposes a novel recurrence plot, namely fuzzy granular recurrence plot (FGRP). An FGRP of a time series is built not directly on the time series itself but on its corre-sponding granular time series which is composed of fuzzy information granules. With specific capability, fuzzy information granules are used as building blocks of an FGRP to achieve high-level, compact and understandable signal models. In order to apply the FGRP method to time series classification tasks, an FGRP based classification model is designed in this paper. Subsequent experiments show that the FGRP of a time series can reduce the effect of noise, and the FGRP based classification model can improve the classification performance. (c) 2023 Elsevier Ltd. All rights reserved.
Keywords:
Fuzzy granular recurrence plot
Recurrence plot
Fuzzy information granule
Noise
Classification
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
ShareSave
ShareSaveShareSaveSemi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveThe design of fuzzy information granules: Tradeoffs between specificity and experimental evidence
PREAI
ShareSaveShareSave
View more