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Autonomous rule induction from data with tolerances in customer relationship management

Tzu-Liang (Bill) Tseng, Chun-Che Huang, Yu-Neng Fan
2011 Expert systems with applications  
However, little work has been devoted to the development of computer-based systems to support CRM in rule induction.  ...  This paper presents a novel rough set based algorithm for automated decision support for CRM.  ...  Li, Hong, and Nahavandi (2003) developed a client classifying algorithm based on rough set theory which reduced the attributes and worked out the reduced decision form to the rules of rough decision.  ... 
doi:10.1016/j.eswa.2010.09.098 fatcat:7ueagkuryrfxdataiypwm2qvgu

Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring

Q SHEN
2004 Pattern Recognition  
This paper proposes a feature selection technique that employs a hybrid variant of rough sets, fuzzy-rough sets, to avoid this information loss.  ...  Experimental results, of applying the present work to complex systems monitoring, show that fuzzy-rough selection is more powerful than conventional entropy-, PCA-and random-based methods.  ...  The authors are very grateful to Alexios Chouchoulas and David Robertson for their support.  ... 
doi:10.1016/s0031-3203(03)00424-2 fatcat:m6ll4nccebfrxap7jxu5znh6hq

Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring

Qiang Shen, Richard Jensen
2004 Pattern Recognition  
This paper proposes a feature selection technique that employs a hybrid variant of rough sets, fuzzy-rough sets, to avoid this information loss.  ...  Experimental results, of applying the present work to complex systems monitoring, show that fuzzy-rough selection is more powerful than conventional entropy-, PCA-and random-based methods.  ...  The authors are very grateful to Alexios Chouchoulas and David Robertson for their support.  ... 
doi:10.1016/j.patcog.2003.10.016 fatcat:j3dmvpyaxrditpdhdhqj77g5iu

Hybrid fuzzy-rough rule induction and feature selection

Richard Jensen, Chris Cornelis, Qiang Shen
2009 2009 IEEE International Conference on Fuzzy Systems  
This paper proposes such an approach, based on fuzzy-rough sets.  ...  The algorithm is experimentally evaluated against leading classifiers, including fuzzy and rough rule inducers, and shown to be effective.  ...  A concept D depends on a set of attribute-value pairs T , if and only if ∅ = ∩{[t]|t ∈ T } ⊆ D (9) T is a minimal complex of D if and only if D depends on T and no proper subset T of T exists such that  ... 
doi:10.1109/fuzzy.2009.5277058 dblp:conf/fuzzIEEE/JensenCS09 fatcat:i6263vsq2ve5bnbswgvuvaa5oy

Fuzzy-Rough Sets Assisted Attribute Selection

Richard Jensen, Qiang Shen
2007 IEEE transactions on fuzzy systems  
One of the many successful applications of rough set theory has been to this area.  ...  This paper investigates a novel approach based on fuzzy-rough sets, fuzzy rough feature selection (FRFS), that addresses these problems and retains dataset semantics.  ...  In rough set theory, dependency is defined in the following way: For , it is said that depends on in a degree , denoted , if If depends totally on , if depends partially (in a degree ) on , and if then  ... 
doi:10.1109/tfuzz.2006.889761 fatcat:bvkjsickofe4hopiq3ti5d2uly

Rough Set Approach in Machine Learning: A Review

Prerna Mahajan, Rekha Kandwal, Ritu Vijay
2012 International Journal of Computer Applications  
The discussion also includes a review of rough set theory in various machine learning techniques like clustering, feature selection and rule induction. General Terms  ...  , intelligent systems, inductive reasoning, pattern recognition, data preprocessing, knowledge discovery, decision analysis, and expert systems.  ...  Nguyen Secondly, a rough set rule induction algorithm generates decision rules, which can reveal profound knowledge and provide new insights [120] .  ... 
doi:10.5120/8924-2996 fatcat:skb3li5syvbe5kgsz63l43xp7a

A rough-fuzzy approach for generating classification rules

Qiang Shen, Alexios Chouchoulas
2002 Pattern Recognition  
This paper presents an approach that integrates a potentially powerful fuzzy rule induction algorithm with a rough set-assisted feature reduction method.  ...  The resulting learned ruleset becomes manageable and may outperform rules learned using more features.  ...  Rough-fuzzy rule induction In essence, the proposed approach deals with patterns involving a large set of features, by applying a dimensionality reduction algorithm on the feature set to discover a smaller  ... 
doi:10.1016/s0031-3203(01)00229-1 fatcat:mz2il2bzbbbdvozxtjjvifbvde

Comparative Overview of Rough Set Toolkit Systems for Data Analysis

Piotr Pięta, Tomasz Szmuc, Krzysztof Kluza, M. Kulisz, M. Szala, M. Badurowicz, W. Cel, M. Chmielewska, Z. Czyż, K. Falkowicz, J. Kujawska, T. Tulwin
2019 MATEC Web of Conferences  
and Rough Set Toolkit for Analysis of Data.  ...  In this paper, we present an analysis of existing rough set tools, namely: Rough Set Exploration System, Rough Sets Data Explorer, Rough Set Data Analysis Framework, Waikato Environment for Knowledge Analysis  ...  rough sets approximation and based on tolerance relations.  ... 
doi:10.1051/matecconf/201925203019 fatcat:x4qnthjoljdmrm7lpntmdfoajm

Finding Rules in Data [chapter]

Tu-Bao Ho
2007 Studies in Classification, Data Analysis, and Knowledge Organization  
One day, Edwin asked me whether one can automatically generate rules for expert systems from data, and I started my new research direction.  ...  In the first year of my preparation for doctor thesis at INRIA in the group of Edwin, I worked on the construction of an inference engine and a knowledge base, by consulting various group members, for  ...  Tolerance rough set model and applications The tolerance rough set model (TRSM) aims to enrich the document representation in terms of semantics relatedness by creating tolerance classes of terms in T  ... 
doi:10.1007/978-3-540-73560-1_36 fatcat:dc3y4j5uoff7rf7giffo4apy6a

Mushroom plant analysis through Reduct Technique

Ayesha butalia, Divya Shah, Dr. R.V Dharaskar
2010 International Journal of Computer Applications  
Lot of knowledge related to the application can be generated through these large data sets. Rough set is the methodology which can be used to deduce rules from these data sets.  ...  The main goal of the rough set analysis is induction of approximations of concepts [4] . Rough sets constitute a sound basis for KDD.  ...  At a general level, there are two types of learning:, inductive and deductive. Inductive machine learning methods create computer programs by extracting rules and patterns out of massive data sets.  ... 
doi:10.5120/123-239 fatcat:crsobli5crfnfe5hqwpls4ud7a

Implementing algorithms of rough set theory and fuzzy rough set theory in the R package "RoughSets"

Lala Septem Riza, Andrzej Janusz, Christoph Bergmeir, Chris Cornelis, Francisco Herrera, Dominik Śle¸zak, José Manuel Benítez
2014 Information Sciences  
Rough set and fuzzy rough set preliminaries In this section, we review some basic notions related to RST and FRST.  ...  An important data mining system for inducing decision rules from various types of data, called Learning from Examples based on Rough Sets (LERS), was created at University of Kansas [30] .  ...  Lastly, it also provides classifier methods based on nearest neighbors [43] and rule induction using hybrid fuzzy-rough rule induction and feature selection (called QuickRules) [49] .  ... 
doi:10.1016/j.ins.2014.07.029 fatcat:5l4ivczoavbmnhcgv7dwdsmk6e

A Survey of Software Packages Used for Rough Set Analysis

Zain Abbas, Aqil Burney
2016 Journal of Computer and Communications  
Rough Set Theory (RST) is a technique used in soft computing that enhances the idea of classical sets to deal with incomplete knowledge and provides a mechanism for concept approximation.  ...  The paper provides a survey of packages that are most frequently used to perform data analysis based on Rough Sets.  ...  It implements rough-set based rule induction as well as a number of additional features such as discretization algorithms, clustering techniques, reduct computation, classifiers, rule pruning and classifier  ... 
doi:10.4236/jcc.2016.49002 fatcat:nkly23aeefcjzihghkvturkr6m

Rough Sets and Rule Induction in Imperfect Information Systems

Do VanNguyen, Koichi Yamada, Muneyuki Unehara
2014 International Journal of Computer Applications  
An algorithm for deriving decision rules based on the rough set models is also studied and proposed.  ...  This probability is then used to define valued tolerance/similarity relations and to develop new rough set models based on the valued tolerance/similarity relations.  ...  RULES INDUCTION Rule induction is one of the most important knowledge discovery techniques in machine learning.  ... 
doi:10.5120/15495-4286 fatcat:lmv2fxjczbhudicpd7wui7dy44

Application of Rough Set Theory in Medical Health Care Data Analytics

Indrani Kumari Sahu, G K Panda, Susant Kumar Das
2019 International Journal of Advanced Science and Technology  
Rough Set theory (RST) is a mathematical tool and used to deal with vagueness, impreciseness, inconsistence and uncertain type knowledge.  ...  RST-based research has been applied in machine learning, inductive reasoning, decision support systems and knowledge discovery applications.  ...   Rule induction Algorithm Decision rules are framed from such set of calculated and available reducts.  ... 
doi:10.33832/ijast.2019.129.03 fatcat:o7nvhyrdzbh37gzdbuduokmxx4

Binary Relations as a Basis for Rule Induction in Presence of Quantitative Attributes

Liping An, Lingyun Tong
2010 Journal of Computers  
of rough approximations based on similarity”, IEEE In order to induce rule set from a decision table Transactions on Knowledge and Data Engineering, vol. involving qualitative and  ...  “Tolerance Rough Sets, Cech Topologies, Management Science and Engineering at Nankai University.  ... 
doi:10.4304/jcp.5.3.440-447 fatcat:dsnecm7nprat3oq3psxtnluydy
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