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Pattern Classification With Granular Computing
2005 IEEE International Conference on Systems, Man and Cybernetics
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This paper puts forward new approaches to solve the pattern classification problems by using granularity computing of quotient space theory. ...
Moreover granular computing is used to solve the classification problems with incomplete information system in this paper. ...
uses granular computing method to solve classification problem with incomplete information. ...
doi:10.1109/icsmc.2005.1571168
dblp:conf/smc/ZhangC05
fatcat:w3bxow2a4jgs5ihi2g5ebtmmbi
A Novel Pattern Classification using Granular Reflex Fuzzy Min-Max Neural Network
2014
International Journal of Computer Applications
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Pattern classification is a system for classifying patterns into dissimilar potential categories. The classifier that is used for classification is granular neural network. ...
A granular neural network called granular reflex fuzzy min-max neural network (GrRFMN). GrRFMN uses hyperbox fuzzy set to signify grainy information. ...
Bargiela, -General fuzzy min-max neural network for clustering and classification,
Table 1 Membership Computation 1 Test Point
P[v,w]
Classification section output
Compensation section output
Class ...
doi:10.5120/18862-0562
fatcat:mzgzdtz7pnag7llb53smkvqxuy
Spatial Associative Classification at Different Levels of Granularity: A Probabilistic Approach
[chapter]
2004
Lecture Notes in Computer Science
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Classification is driven by spatial association rules discovered at multiple granularity levels. ...
It performs the classification at different granularity levels and takes advantage from domain specific knowledge in form of rules that support qualitative spatial reasoning. ...
would like to thank Jim Petch, Keith Cole and Mohammed Islam (University of Manchester) for expert collection, collation, editing and delivery of the several data sets made available through Manchester Computing ...
doi:10.1007/978-3-540-30116-5_12
fatcat:yy2eo6ux4ndwrpzzlxwriavfiy
Rough sets and fuzzy sets in natural computing
2011
Theoretical Computer Science
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, granular computing and perception-based computing. ...
In this consortium, rough sets and fuzzy sets work synergistically, often with other soft computing approaches, and use the principle of granular computing. ...
Pizzi, discusses a preprocessing method for pattern classification that replaces a feature value with the respective degrees of belongingness to a collection of fuzzy sets overlapping at the respective ...
doi:10.1016/j.tcs.2011.05.036
fatcat:ptgtfw6wrncz7nqm6rci6p4lrm
A force-driven granular model for EMG based grasp recognition
2017
2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
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This paper proposes a solution to tackle the challenge with a force-driven granular model (FDGM).The problem of nclass hand grasp classification has been represented as forcebased granular modelling, in ...
In comparison with other rules of information granulation, it is confirmed that the force-driven rule is of the most efficiency with comparable classification accuracy. ...
FDGM vs other clustering based granular models This experiment compares FDGM with other clustering based granular models in terms of computing complexity and classification accuracy. ...
doi:10.1109/smc.2017.8123074
dblp:conf/smc/FangZLJL17
fatcat:2d5lu4fptnfyhbtqouofzccssu
Fast and Effective Spam Sender Detection with Granular SVM on Highly Imbalanced Mail Server Behavior Data
2006
2006 International Conference on Collaborative Computing: Networking, Applications and Worksharing
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Due to the large amount of bad senders, this classification task has to cope with highly imbalanced data. ...
In this research, we explore a behavioral classification approach based on spectral sender characteristics retrieved from such global messaging patterns. ...
There are two principles in granular computing. ...
doi:10.1109/colcom.2006.361856
dblp:conf/colcom/TangKJZ06
fatcat:rsq4wp6h65etrdjgsjtaor3pbq
using multiple losses for accurate facial age estimation
[article]
2021
arXiv
pre-print
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Age estimation is an essential challenge in computer vision. With the advances of convolutional neural networks, the performance of age estimation has been dramatically improved. ...
The method combines four classification losses and one regression loss representing different class granularities together, and we name it as Age-Granularity-Net. ...
Fig. 3 . 3 Example images with five years as the age granularity in CVPR ChaLearn 2016 dataset after face detection, alignment and data augmentation.conference on computer vision and pattern recognition ...
arXiv:2106.09393v1
fatcat:otiyojflsjhitfhers7e7if4am
Fuzzy rule-based systems for recognition-intensive classification in granular computing context
2018
Granular Computing
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In particular, we position the study in the context of granular computing, and propose the use of fuzzy rule-based systems for recognition-intensive classification of real-life data instances. ...
In this paper, we focus on classification problems that involve pattern recognition. ...
Acknowledgements The authors acknowledge support from the Social Data Science Lab at the Cardiff University and the Affective and Smart Computing Research Group at the Northumbria University. ...
doi:10.1007/s41066-018-0076-7
fatcat:gbcd7loc4jf3tok5sidgb3wzca
Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition
2008
Pattern Recognition
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The 2ν-GSVM performs accurate classification which is subsequently used to dynamically compute the weights of visible and infrared images for generating a fused face image. 2D log polar Gabor transform ...
granularity levels and resolution. ...
We first propose the formulation of 2ν-Granular Support Vector Machine (2ν-GSVM) for pattern classification which is used in the proposed image fusion algorithm. ...
doi:10.1016/j.patcog.2007.06.022
fatcat:bwgkinwr55f3jnkxihc5evupga
Spatial associative classification: propositional vs structural approach
2006
Journal of Intelligent Information Systems
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In the latter, the Bayesian framework is extended following a multi-relational data mining approach in order to cope with spatial classification tasks. ...
Third, spatial objects can be considered at different levels of abstraction (or granularity). ...
This suggests evaluating the class with computing probabilities according to all the rules. ...
doi:10.1007/s10844-006-9950-x
fatcat:eywngvprbzhohctl7ef7qfoekm
Mining Relational Association Rules for Propositional Classification
[chapter]
2005
Lecture Notes in Computer Science
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It performs the classification at different granularity levels and takes advantage from domain specific knowledge in form of hierarchies and rules. ...
Propositionalisation based on relational association rules discovery is implemented in a relational classification framework, named MSRC, tightly integrated with a relational database. ...
SPADA takes advantage of statistics computed at granularity level l when computing the supports of patterns at granularity level l+1. ...
doi:10.1007/11558590_53
fatcat:ngj2cb2btfao3ibxyvg3mss2oi
Towards a theory of granular sets
[article]
2014
arXiv
pre-print
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It is realized that in any hierarchical classification problem, Granular set naturally arises. ...
It is reasoned that in classification problem arising in an information system (represented by information table), a novel set called Granular set naturally arises. ...
Now we discuss how granular sets naturally arise in classification problems ( e.g. Pattern Recognition, databases ) associated with various applications. ...
arXiv:1406.4324v1
fatcat:bnku43n7rzg6rly5bllvu7js5i
A Granular Computing Based Classification Method From Algebraic Granule Structure
2021
IEEE Access
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The proposed granular computing based classification method provides a general framework for classifying granularity with algebraic granule structure, enriches granular computing theory from granule structure ...
Granular computing has now been widely applied in image processing [3] , machine learning [4] , complex problem solving [5] , pattern recognition [6] , intelligent control [7] , artificial neural ...
.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS structure, so that it can provide a more broad systematic framework for classifying algebraic structure based granularity. ...
doi:10.1109/access.2021.3077409
fatcat:atbinysqpbgsfmw3w4l3gykg6m
Looking deeper into Time for Activities of Daily Living Recognition
2020
2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
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The temporal structure is represented globally by different temporal granularities and locally by temporal segments. ...
We also propose a two-level pose driven attention mechanism to take into account the relative importance of the segments and granularities. ...
Acknowledgement We are grateful to INRIA Sophia Antipolis -Mediterranean "NEF" computation cluster for providing resources and support. ...
doi:10.1109/wacv45572.2020.9093575
dblp:conf/wacv/DasTB20
fatcat:vknggzstnnbwvhf2vo6k244pki
Tree++: Truncated Tree Based Graph Kernels
[article]
2020
arXiv
pre-print
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Our evaluation on a variety of real-world graphs demonstrates that Tree++ achieves the best classification accuracy compared with previous graph kernels. ...
The path-pattern graph kernel can only capture graph similarity at fine granularities. ...
The path-pattern graph kernel can only capture graph similarity at fine granularities. ...
arXiv:2002.09846v1
fatcat:26tv5wres5dkfoldg4ktkfveq4
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