scispace - formally typeset
Search or ask a question
Posted ContentDOI

Fuzzy Rough Sets and Its Application in Data Mining Field

TL;DR: This paper discusses about rough sets and fuzzy rough sets with its applications in data mining that can handle uncertain and vague data so as to reach at meaningful conclusions.
Abstract: Rough set theory is a new method that deals with vagueness and uncertainty emphasized in decision making. The theory provides a practical approach for extraction of valid rules fromdata.This paper discusses about rough sets and fuzzy rough sets with its applications in data mining that can handle uncertain and vague data so as to reach at meaningful conclusions.

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI
TL;DR: This research study applies the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures and develops and implements some algorithms of the proposed models to granulate these social networks.
Abstract: An extraction of granular structures using graphs is a powerful mathematical framework in human reasoning and problem solving. The visual representation of a graph and the merits of multilevel or multiview of granular structures suggest the more effective and advantageous techniques of problem solving. In this research study, we apply the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures. We discuss the accuracy measures of rough fuzzy approximations and measure the distance between lower and upper approximations. Moreover, we consider the adjacency matrix of a rough fuzzy digraph as an information table and determine certain indiscernible relations. We also discuss some general geometric properties of these indiscernible relations. Further, we discuss the granulation of certain social network models using rough fuzzy digraphs. Finally, we develop and implement some algorithms of our proposed models to granulate these social networks.

13 citations

Journal ArticleDOI
TL;DR: The data mining of the traffic vehicles with rough set theory was made and it was shown that it is possible to generate the decision rules of the number of vehicles at the specific points in the city.
Abstract: Often, it is difficult to interpret and use the large size of data obtained from the experiment. In addition, the generated information can be unprecise. The rough set theory besides probability theory, fuzzy set theory and many others in recent years is very often used by scientists to solve problems of data mining. In the paper the data mining of the traffic vehicles with rough set theory was made. With this theory it was shown that it is possible to generate the decision rules of the number of vehicles at the specific points in the city. On the basis of 120 objects 16 well-defined linguistic decision rules were obtained.

7 citations

Proceedings ArticleDOI
01 Aug 2017
TL;DR: This paper briefly explains the abstract of fuzzy sets, advanced fuzzy sets and their hybrids, which include rough sets, intuitionistic fuzzy set, interval-valued fuzzy setsand soft sets.
Abstract: Fuzzy Sets were proposed several years ago with various extensions in later years. Each extension has advantages over the fuzzy sets. Rough sets are used to handle incomplete information. Interval-valued fuzzy sets deal with uncertainty and vagueness. Intuitionistic fuzzy sets contain a sub-interval hesitation degree that lies between membership and non-membership degrees. Soft sets overcome the problem of insufficiency of parameterization. Advanced fuzzy sets have myriad number of advantages due to their applications in realistic examples. This paper briefly explains the abstract of fuzzy sets, advanced fuzzy sets and their hybrids. Advanced fuzzy sets include rough sets, intuitionistic fuzzy sets, interval-valued fuzzy sets and soft sets.

5 citations


Cites background from "Fuzzy Rough Sets and Its Applicatio..."

  • ...[7] have suggested some of the usage of fuzzy rough sets in data mining....

    [...]

Book ChapterDOI
06 Dec 2018
TL;DR: This paper proposes a hybrid novel method for handling the handoff mechanism based on Fuzzy rough set theory (FRST) with Support Vector machine (SVM), which enables the decision making stage of the handoffs process more tenable and productive.
Abstract: Spectrum handoff management is an important issue which needs to be addressed in Cognitive Radio Networks (CRN) for interminable connectivity and productive usage of unallocated spectrum for the unlicensed users. Spectrum handoff which comes under the phase of Spectrum mobility in CRN plays a vital role in ensuring seamless connectivity which is quite exigent. Handoff process in general comes under active and proactive types. The intelligent and hybrid handoff methods which combines both these types based on the network conditions proves to be quite satisfactory in the recent works. This paper proposes a hybrid novel method for handling the handoff mechanism based on Fuzzy rough set theory (FRST) with Support Vector machine (SVM), which enables the decision making stage of the handoff process more tenable and productive. The implied method predicts the node wherein handoff is to be initiated in the lead through which the handoff delay time and number of handoffs are minimized. The experimental results are compared with the previously proposed hybrid schemes including Fuzzy genetic algorithm (FGA) based handoff, FGA with cuckoo search (CS) optimization technique, FRS with CS and the findings portray the suggested methodology attains better prediction mechanism with minimal handoffs.

4 citations

Journal ArticleDOI
TL;DR: The developed S2M-WSN system would definitely bridge the information gap between end-users and domain experts for sustainable growth of agriculture.
Abstract: This paper presents the design of a (i) soil sensing and monitoring wireless sensor network (S2M-WSN) that can sense and monitor farmland with distributed and networked motes equipped with multiple soil sensors and (ii) a prototype decision support system (DSS) that analyses the soil data received from S2M-WSN using statistical methods, stored rules and knowledge base (KB) and auto-transmits alerts on the mobile phones of end-users. Both S2M-WSN and the DSS are integrated through (iii) predefined data frame format and interface protocol. The developed system has been deployed both in the laboratory and real crop conditions for verification, validation and performance evaluation. The system is evaluated by considering various performance metrics such as ambiguities associated with data, data packet delivery rates, support delivery performance ratio, system response time, and resultant plant growth. The developed system empowers the end users with assistance in on-field decision making through relevant advisories and alerts at right time on their mobile phones. The authors believe that the developed system would definitely bridge the information gap between end-users and domain experts for sustainable growth of agriculture.

4 citations

References
More filters
Journal ArticleDOI
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Abstract: Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.

7,185 citations

Journal ArticleDOI
TL;DR: An example of the rough set theory application to the QSAR classification problem is presented and the basic concepts of therough set theory are introduced and adequately illustrated.

339 citations


"Fuzzy Rough Sets and Its Applicatio..." refers background in this paper

  • ...A set of reducts are all possible minimal subsets of attributes, which lead to the same number of elementary sets as the whole set of attributes [2,4]....

    [...]

  • ...The core is the common part of all reducts or the intersection of different reducts gives the core of the attributes [2,4]....

    [...]

Dissertation
01 Jan 2004
TL;DR: This thesis proposes and develops an approach based on fuzzy-rough sets, fuzzy rough feature selection (FRFS), that addresses problems and retains dataset semantics and is shown to equal or improve classification accuracy when compared to the results from unreduced data.
Abstract: Feature selection (FS) refers to the problem of selecting those input attributes that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods, feature selectors preserve the original meaning of the features after reduction. This has found application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and web content classification. FS techniques have also been applied to small and medium-sized datasets in order to locate the most informative features for later use. Many feature selection methods have been developed and are reviewed critically in this thesis, with particular emphasis on their current limitations. The leading methods in this field are presented in a consistent algorithmic framework. One of the many successful applications of rough set theory has been to this area. The rough set ideology of using only the supplied data and no other information has many benefits in FS, where most other methods require supplementary knowledge. However, the main limitation of rough set-based feature selection in the literature is the restrictive requirement that all data is discrete. In classical rough set theory, it is not possible to consider real-valued or noisy data. This thesis proposes and develops an approach based on fuzzy-rough sets, fuzzy rough feature selection (FRFS), that addresses these problems and retains dataset semantics. Complexity analysis of the underlying algorithms is included. FRFS is applied to two domains where a feature reducing step is important; namely, web content classification and complex systems monitoring. The utility of this approach is demonstrated and is compared empirically with several dimensionality reducers. In the experimental studies, FRFS is shown to equal or improve classification accuracy when compared to the results from unreduced data. Classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method. In addition, it is shown that FRFS is more powerful than the other FS techniques in the comparative study.

162 citations


"Fuzzy Rough Sets and Its Applicatio..." refers background in this paper

  • ...The fuzzylower and upper approximations aregiven as [7,8]:...

    [...]

Journal ArticleDOI
TL;DR: The proposed algorithms to find reducts that are based on the minimal elements in the discernibility matrix are developed in the framework of fuzzy rough sets and Experimental comparison shows that the proposed algorithms are effective.
Abstract: Attribute reduction is one of the most meaningful research topics in the existing fuzzy rough sets, and the approach of discernibility matrix is the mathematical foundation of computing reducts. When computing reducts with discernibility matrix, we find that only the minimal elements in a discernibility matrix are sufficient and necessary. This fact motivates our idea in this paper to develop a novel algorithm to find reducts that are based on the minimal elements in the discernibility matrix. Relative discernibility relations of conditional attributes are defined and minimal elements in the fuzzy discernibility matrix are characterized by the relative discernibility relations. Then, the algorithms to compute minimal elements and reducts are developed in the framework of fuzzy rough sets. Experimental comparison shows that the proposed algorithms are effective.

161 citations


"Fuzzy Rough Sets and Its Applicatio..." refers methods in this paper

  • ...Finally, novel algorithms to find proper reducts with the minimal elementsare designed [11]....

    [...]

Journal ArticleDOI
TL;DR: A hybrid scheme that combines the advantages of fuzzy sets and rough sets in conjunction with statistical feature extraction techniques is introduced that performs well reaching over 98% in overall accuracy with minimal number of generated rules.

139 citations


"Fuzzy Rough Sets and Its Applicatio..." refers methods in this paper

  • ...The experimental results show that the hybrid scheme applied in this study performs well reaching over 98% in overall accuracy with minimal number of generated rules [12]....

    [...]