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Pattern Classification With Granular Computing

Min Zhang, Jia-Xing Cheng
2005 IEEE International Conference on Systems, Man and Cybernetics  
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

Ramesh Kakollu, E. Vargil Vijay
2014 International Journal of Computer Applications  
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]

Michelangelo Ceci, Annalisa Appice, Donato Malerba
2004 Lecture Notes in Computer Science  
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

Hung Son Nguyen, Sankar K. Pal, Andrzej Skowron
2011 Theoretical Computer Science  
, 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

Yinfeng Fang, Dalin Zhou, Kairu Li, Zhaojie Ju, Honghai Liu
2017 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)  
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

Yuchun Tang, Sven Krasser, Paul Judge, Yan-Qing Zhang
2006 2006 International Conference on Collaborative Computing: Networking, Applications and Worksharing  
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]

Yi Zhou, Heikki Huttunen, Tapio Elomaa
2021 arXiv   pre-print
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

Han Liu, Li Zhang
2018 Granular Computing  
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

Richa Singh, Mayank Vatsa, Afzel Noore
2008 Pattern Recognition  
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

Michelangelo Ceci, Annalisa Appice
2006 Journal of Intelligent Information Systems  
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]

Annalisa Appice, Michelangelo Ceci, Donato Malerba
2005 Lecture Notes in Computer Science  
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]

Garimella Rama Murthy
2014 arXiv   pre-print
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

Linshu Chen, Lei Zhao, Zhenguo Xiao, Yuanhui Liu, Jiayang Wang
2021 IEEE Access  
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

Srijan Das, Monique Thonnat, Francois Bremond
2020 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)  
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]

Wei Ye, Zhen Wang, Rachel Redberg, Ambuj Singh
2020 arXiv   pre-print
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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