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A Soft-Rough Set Based Approach for Handling Contextual Sparsity in Context-Aware Video Recommender Systems

Syed Manzar Abbas, Khubaib Amjad Alam, Shahaboddin Shamshirband
2019 Mathematics  
The proposed model will select a minimal set of influencing contexts using a weights assign process by Soft-Rough sets.  ...  For the purpose of improving the accuracy of recommendation process, we have proposed a novel contextual information selection process using Soft-Rough Sets.  ...  Conflicts of Interest: The authors declare no conflict of interest in the publication of this article.  ... 
doi:10.3390/math7080740 fatcat:qxhkmo26jrfuhfpumr2s42hugi

Data Warehousing for Rough Web Caching and Pre-fetching

Sarina Sulaiman, Siti Mariyam Shamsuddin, Ajith Abraham
2010 2010 IEEE International Conference on Granular Computing  
In order to get the hit ratio, rough set algorithms are applied to mark the lower and upper approximation and to get the best results.  ...  This situation causes much lagging in the current workplace or geographic area. One of the infrastructures that support and convey data is a proxy server.  ...  The authors would like to thank Ministry of Science, Technology and Innovation Malaysia (MOSTI) and Reseach Management Centre (RMC), UTM for their kind financial support under the escience fund.  ... 
doi:10.1109/grc.2010.138 dblp:conf/grc/SulaimanSA10 fatcat:jajz7j7tn5hhdc6jiha5x2pnzu

Application of GIS-Interval Rough AHP Methodology for Flood Hazard Mapping in Urban Areas

Ljubomir Gigović, Dragan Pamučar, Zoran Bajić, Siniša Drobnjak
2017 Water  
The first of them uses a new approach to the exploitation of uncertainty in the application of the AHP technique, the interval rough numbers (IR'AHP).  ...  Because of the subjectivity and ambiguity that occur in group decision-making, this paper used the interval rough numbers, combined with the AHP method in order to exploit that subjectivity.  ...  Dragan Pamučar and Zoran Bajić performed the simulation of the multi-criteria and the methodology. The discussions and analysis were carried out by all authors.  ... 
doi:10.3390/w9060360 fatcat:mfu5z2xhfvajrkwcc6sfh4kmyy

A Rough Set Based Feature Selection on KDD CUP 99 Data Set

Vinod Rampure, Akhilesh Tiwari
2015 International Journal of Database Theory and Application  
KDD CUP' 99 Data set is the most widely used dataset in research domain.  ...  Selecting important feature on the basis of rough set based feature selection approach have lead to a simplification of the problem, faster and more accurate detection rates.  ...  (upper) approximation of the set X.  ... 
doi:10.14257/ijdta.2015.8.1.16 fatcat:hmhcty7ybrdbvk4x65kxzod75e

Page 10284 of Mathematical Reviews Vol. , Issue 2004m [page]

2004 Mathematical Reviews  
In the constructive approach, a pair of lower and upper generalized fuzzy rough (and rough fuzzy, respectively) approximation op- erators is first defined.  ...  Summary: “This paper presents a general framework for the study of rough set approximation operators in fuzzy environments in which both constructive and axiomatic approaches are used.  ... 

Granular computing, rough entropy and object extraction

Sankar K. Pal, B. Uma Shankar, Pabitra Mitra
2005 Pattern Recognition Letters  
The problem of image object extraction in the framework of rough sets and granular computing is addressed. A measure called "rough entropy of image" is defined based on the concept of image granules.  ...  Its maximization results in minimization of roughness in both object and background regions; thereby determining the threshold of partitioning.  ...  The set approximation capability of rough sets is exploited in the present investigation to formulate an entropy measure, called rough entropy, quantifying the uncertainty in an object-background image  ... 
doi:10.1016/j.patrec.2005.05.007 fatcat:hftpiv5xufhwbp6hovtyl74n6i


S.K Pal, A Skowron
2003 Pattern Recognition Letters  
The idea is to approximate any concept (a crisp subset of the domain) by a pair of exact sets, called the lower and upper approximations, in the granular universe.  ...  Flexibility offered by rough sets is then exploited to develop a rough PC-Tree which reduces the processing time and memory requirements without affecting the classification accuracy.  ... 
doi:10.1016/s0167-8655(02)00195-2 fatcat:lvns5acycvaapfxwwvobe2x7ju

Enhancing the Capability of IDS using Fuzzy Rough Classifier with Genetic Search Feature Reduction

Ashalata Panigrahi, Manas Ranjan Patra
2014 Transactions on Networks and Communications  
Tsang et al. [5] in their work defines attribute reduction with fuzzy rough sets and analyzes its structures in details and they have developed a formal definition of reduction with fuzzy rough sets.  ...  Then, the simple feature selection procedure was applied to eliminate unimportant features from the training set. Finally, the obtained SVM model classifies the network traffic data.  ...  METHODOLOGY Hybridization of Rough Sets and Fuzzy sets Fuzzy Set A fuzzy set in X is an X → [0, 1] mapping, while a fuzzy relation in X is a fuzzy set in X × X.  ... 
doi:10.14738/tnc.22.97 fatcat:mjunm7lqgrfdxnkp4lplam3kee

A Novel Neighborhood Rough Set Based Classification Approach for Medical Diagnosis

S. Udhaya kumar, H. Hannah Inbarani
2015 Procedia Computer Science  
Disease prediction and decision making plays asignificant role in medical diagnosis. In this study, a novel neighborhood rough set classification approach is presented to deal with medical datasets.  ...  with other existing approaches such as rough set, nearest neighbor, support vector machine, Back propagation algorithm and multilayer perceptron to conclude that the proposed approach is cheaper way for  ...  Acknowledgement The first author immensely acknowledges the partial financial assistance under University Research Fellowship, Periyar University, Salem.  ... 
doi:10.1016/j.procs.2015.03.216 fatcat:ftdaqvpgxjdvxbmwhkdylmtehm

Ontology Engineering with Rough Concepts and Instances [chapter]

C. Maria Keet
2010 Lecture Notes in Computer Science  
To fill this gap, we use OWL 2 and their application infrastructures together with rough sets.  ...  Although OWL 2 QL is insufficient to represent most of rough set's semantics, the mapping layer of its Ontology-Based Data Access framework that links concepts in the ontology to queries over the data  ...  Acknowledgements I thank Umberto Straccia, Ferdinando Bobillo, and Mariano Rodríguez-Muro for feedback during the experimentation.  ... 
doi:10.1007/978-3-642-16438-5_40 fatcat:fssri5wzyzfbtkdmgppfvl4glm

An In-Database Rough Set Toolkit

Frank Beer, Ulrich Bühler
2015 Lernen, Wissen, Daten, Analysen  
The Rough Set Theory is a common methodology to discover hidden patterns in data.  ...  As such we leverage the efficient data structures and algorithms provided by that systems. Thereby we introduce a baseline framework for in-database mining supported by Rough Set Theory.  ...  The authors deeply thank Maren and Martin who provided expertise and excellent support in the initial phase of this work.  ... 
dblp:conf/lwa/BeerB15 fatcat:dn2jdh6wsbbdzj7xvmytvaboxa

Spatiotemporal Approach for Tracking Using Rough Entropy and Frame Subtraction [chapter]

B. Uma Shankar, Debarati Chakraborty
2011 Lecture Notes in Computer Science  
Then the intersection of the temporal segmentation and spatial segmentation for the same frame is analyzed in RGB feature space.  ...  We present here an approach for video image segmentation where spatial segmentation is based on rough sets and granular computing and temporal segmentation is done by consecutive frame subtraction.  ...  The set approximation capability of rough sets is exploited in this investigation [7, 12] to formulate an entropy measure, called rough entropy, quantifying the uncertainty in an object-background image  ... 
doi:10.1007/978-3-642-21786-9_33 fatcat:fjgpd2klvbfx7ilhbfxl3xz5z4

A Rough-Fuzzy HSV Color Histogram for Image Segmentation [chapter]

Alessio Ferone, Sankar Kumar Pal, Alfredo Petrosino
2011 Lecture Notes in Computer Science  
The segmentation is performed by partitioning each block in multiple rough fuzzy sets that are used to build a lower and a upper histogram in the HSV color space.  ...  A color image segmentation technique which exploits a novel definition of rough fuzzy sets and the rough-fuzzy product operation is presented.  ...  In a rough-set theoretic sense, the histon represents the upper approximation of the color regions, that is a collection of pixels possibly belonging to the same region, while the histogram represents  ... 
doi:10.1007/978-3-642-24085-0_4 fatcat:yd3hutaaxff6janelomvd67lbi

Rough Sets, Kernel Set, and Spatiotemporal Outlier Detection

Alessia Albanese, Sankar K. Pal, Alfredo Petrosino
2014 IEEE Transactions on Knowledge and Data Engineering  
The proposed method, called Rough Outlier Set Extraction (ROSE), relies on a rough set theoretic representation of the outlier set using the rough set approximations, i.e., lower and upper approximations  ...  In this paper, we deal with the outlier detection problem in spatiotemporal data and describe a rough set approach that finds the top outliers in an unlabeled spatiotemporal data set.  ...  In contrast, our method, called Rough Outlier Set Extraction (ROSE), exploits the set-oriented point of view of rough set theory to define the concept of outlier in terms of its lower and upper approximations  ... 
doi:10.1109/tkde.2012.234 fatcat:nyrmgokz7rgyffjtkf7jbkfoyi

Rough sets and fuzzy sets in natural computing

Hung Son Nguyen, Sankar K. Pal, Andrzej Skowron
2011 Theoretical Computer Science  
In this consortium, rough sets and fuzzy sets work synergistically, often with other soft computing approaches, and use the principle of granular computing.  ...  Rough sets and fuzzy sets in natural computing Natural Computing (NC) is a discipline that builds a bridge between computer science and natural sciences.  ...  lower and upper approximations of fuzzy rough set theory.  ... 
doi:10.1016/j.tcs.2011.05.036 fatcat:ptgtfw6wrncz7nqm6rci6p4lrm
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