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Rough Sets in Knowledge Discovery and Data Mining(<特集>ラフ集合の理論と応用)

Ning ZHONG
2001 Journal of Japan Society for Fuzzy Theory and Systems  
Thus ILP has to deal sN'ith imperfect data.  ...  In this aspect, the theory, tech-GDTRS-ILP At the current stage, we apply GDT and rough set theory to ILP to deal with some kinds of imperfect data occurring in large real-world applications [16].  ... 
doi:10.3156/jfuzzy.13.6_581 fatcat:win6ytns7nesfoggojlp5iauee

Design and Implementation of Rough Set Algorithms on FPGA: A Survey

Kanchan Shailendra, Ashwin. G.
2014 International Journal of Advanced Research in Artificial Intelligence (IJARAI)  
Rough set theory, developed by Z. Pawlak, is a powerful soft computing tool for extracting meaningful patterns from vague, imprecise, inconsistent and large chunk of data.  ...  Conventional Rough set information processing like discovering data dependencies, data reduction, and approximate set classification involves the use of software running on general purpose processor.  ...  FPGA offers a promising solution to deal with such kind of problems as rough set algorithms are inherently parallel. Thus these algorithms can be effectively mapped on FPGA.  ... 
doi:10.14569/ijarai.2014.030903 fatcat:t4wxelzcizau7f4txgf2iekleq

An Efficient Algorithm for Haplotype Inference on Pedigrees with a Small Number of Recombinants

Jing Xiao, Tiancheng Lou, Tao Jiang
2011 Algorithmica  
The problem is NP-hard, although it is known that the number of recombinants in a practical dataset is usually very small.  ...  n 3 , the algorithm can correctly find a feasible haplotype configuration that obeys the Mendelian law of inheritance and requires no more than k recombinants with probability 1 − O(k 2 log 2 n mn + 1  ...  Acknowledgements We are very grateful to the annonymous referees for their many constructive suggestions and comments. The research was supported in part by NIH grant 2R01LM008991, NSF  ... 
doi:10.1007/s00453-011-9494-5 fatcat:fmwqde2jjngznov5wa5guejtlm

A Review of Rule Learning Based Intrusion Detection Systems and Their Prospects in Smart Grids

Qi Liu, Veit Hagenmeyer, Hubert B. Keller
2021 IEEE Access  
With misuse based IDS' inability to detect unknown attacks and requirement for frequently manually crafting and updating signatures and with anomaly based IDS' bad reputation for high false alarm rate,  ...  We argue that specification based IDS especially using rule learning could prove to be the most promising IDS for SG.  ...  ROUGH SET THEORY Although often contrasted with fuzzy set theory, rough set theory, introduced by Pawlak [55] , is another independent approach to imperfect knowledge, and their relationship should be  ... 
doi:10.1109/access.2021.3071263 fatcat:4knmfd4yezhabkbnsl5ahdt2bi

Mining scientific data [chapter]

Naren Ramakrishnan, Ananth Y. Grama
2002 Advances in Computers  
Coupled with the availability of massive storage systems and fast networking technology to manage and assimilate data, these have given a significant impetus to data mining in the scientific domain.  ...  The past two decades have seen rapid advances in high performance computing and tools for data acquisition in a variety of scientific domains.  ...  Areas that deal most directly with this perspective include Sections 3 and 5.  ... 
doi:10.1016/s0065-2458(01)80028-0 fatcat:sbp5xai2yva2pcx3lj2sq64yma

Analysis of Possibility Theory for Reasoning under Uncertainty

Andrew Adewale Alola, Mustafa Tunay, Violet Alola
2013 International Journal of Statistics and Probability  
I certify that this thesis satisfies the requirements as a thesis for the degree of Master of Science in Applied Mathematics and Computer Science.  ...  problem setting for the inductive logic programming (ILP) to obtain more useful but rough information from such imperfect data.  ...  Rough set theory is applied as a solution to imperfect data in inductive logic programming especially because the application of rough problem settings ease the necessary requirements in the standard normal  ... 
doi:10.5539/ijsp.v2n2p12 fatcat:nzuvwhjl2fhgtezpkojj4gkghu

From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group) [article]

Zied Bouraoui and Antoine Cornuéjols and Thierry Denœux and Sébastien Destercke and Didier Dubois and Romain Guillaume and João Marques-Silva and Jérôme Mengin and Henri Prade and Steven Schockaert and Mathieu Serrurier and Christel Vrain
2019 arXiv   pre-print
high level inference, knowledge graph completion, declarative frameworks for data mining, or preferences and recommendation).  ...  Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal  ...  However, ILP suffers from two drawbacks: the complexity of its algorithms and its inability to deal with uncertain data.  ... 
arXiv:1912.06612v1 fatcat:yfnx3pzs6jhxtggaylc76pwjc4

Evolutionary approaches to fuzzy modelling for classification

MICHELLE GALEA, QIANG SHEN, JOHN LEVINE
2004 Knowledge engineering review (Print)  
This is set in one of two contexts: overcoming the knowledge acquisition bottleneck in the development of intelligent reasoning systems, and in the data mining of databases where the aim is the discovery  ...  The different strategies utilizing evolutionary algorithms for knowledge acquisition are abstracted from the work reviewed.  ...  Taken together, these rules form a complete classifier capable of dealing with the whole problem.  ... 
doi:10.1017/s0269888904000189 fatcat:vephvmrhkrefjglkhvag4iz4hi

Power optimizations for the MLCA using dynamic voltage scaling

Ivan Matosevic, Tarek S. Abdelrahman, Faraydon Karim, Alain Mellan
2005 Proceedings of the 2005 workshop on Software and compilers for embedded systems - SCOPES '05  
Power Optimizations for the MLCA Using Dynamic Voltage Scaling The Multi-Level Computing Architecture (MLCA) is a novel architecture for parallel systems-on-a-chip.  ...  Abdelrahman, for his guidance and support throughout the course of this work.  ...  The first set is used as the training set, and our technique is then applied to both sets with parameters computed from the profiling data for the training set.  ... 
doi:10.1145/1140389.1140401 dblp:conf/scopes/MatosevicAKM05 fatcat:h5hpabudg5dmxkzdhiv2i6kpx4

Optimization of Potable Water Distribution and Wastewater Collection Networks: A Systematic Review and Future Research Directions

Wanqing Zhao, Thomas H. Beach, Yacine Rezgui
2016 IEEE Transactions on Systems, Man & Cybernetics. Systems  
for optimizing water networks.  ...  Firstly, both WDNs and WWCNs are conceptually and functionally described along with illustrative benchmarks.  ...  ACKNOWLEDGMENT The authors would like to thank the Editors and the reviewers for their most constructive comments and suggestions to improve the quality of the paper.  ... 
doi:10.1109/tsmc.2015.2461188 fatcat:apmravbwyrbr5l75bflc2j6gme

Cognitive Vision: Integrating Symbolic Qualitative Representations with Computer Vision [chapter]

A. G. Cohn, D. C. Hogg, B. Bennett, V. Devin, A. Galata, D. R. Magee, C. Needham, P. Santos
2006 Lecture Notes in Computer Science  
A number of researchers have attempted to deal with object occlusion (and the resultant tracking problems) by attempting to track through occlusion.  ...  A perfect rule-set is generated for experiment 3 with object noise; however some rules are lost with the introduction of utterance noise.  ... 
doi:10.1007/11414353_14 fatcat:c4n2r2rhe5bvbcffejouhiziae

Argument-Based Machine Learning [chapter]

Ivan Bratko, Martin Možina, Jure Žabkar
2006 Lecture Notes in Computer Science  
This approach combines machine learning from examples with concepts from the field of argumentation. The idea is to provide expert's arguments, or reasons, for some of the learning examples.  ...  We implement ABCN2, an argument-based extension of the CN2 rule learning algorithm, conduct experiments and analyze its performance in comparison with the original CN2 algorithm.  ...  Introduction A fundamental problem of machine learning is dealing with large spaces of possible hypotheses.  ... 
doi:10.1007/11875604_2 fatcat:ibijntzc3zav3klawjbsztu5bm

Argument based machine learning

Martin Možina, Jure Žabkar, Ivan Bratko
2007 Artificial Intelligence  
This approach combines machine learning from examples with concepts from the field of argumentation. The idea is to provide expert's arguments, or reasons, for some of the learning examples.  ...  We implement ABCN2, an argument-based extension of the CN2 rule learning algorithm, conduct experiments and analyze its performance in comparison with the original CN2 algorithm.  ...  Introduction A fundamental problem of machine learning is dealing with large spaces of possible hypotheses.  ... 
doi:10.1016/j.artint.2007.04.007 fatcat:7h4oqt4nyfclhk65o5wyv2wuw4

Argument-Based Machine Learning [chapter]

Ivan Bratko, Jure Žabkar, Martin Možina
2009 Argumentation in Artificial Intelligence  
This approach combines machine learning from examples with concepts from the field of argumentation. The idea is to provide expert's arguments, or reasons, for some of the learning examples.  ...  We implement ABCN2, an argument-based extension of the CN2 rule learning algorithm, conduct experiments and analyze its performance in comparison with the original CN2 algorithm.  ...  Introduction A fundamental problem of machine learning is dealing with large spaces of possible hypotheses.  ... 
doi:10.1007/978-0-387-98197-0_23 fatcat:szo6r3uzkbb73k56xmlwem22ju

Multiple criteria ranking and choice with all compatible minimal cover sets of decision rules

Miłosz Kadziński, Roman Słowiński, Salvatore Greco
2015 Knowledge-Based Systems  
We introduce a new multiple criteria ranking/choice method that applies Dominance-based Rough Set Approach (DRSA) and represents the Decision Maker's (DM's) preferences with decision rules.  ...  Then, all minimal-cover (MC) sets of decision rules being compatible with this preference information are induced.  ...  Conclusions We presented a new approach for dealing with multiple criteria ranking problems.  ... 
doi:10.1016/j.knosys.2015.09.004 fatcat:fouwt5ccn5ewnje2ga2cut32sm
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