An Innovative Approach for Attribute Reduction in Rough Set Theory

Pessoa, Alex Sandro Aguiar; Stephany, Stephan

doi:10.4236/iim.2014.65022

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Rough Sets is an effective mathematical tool to deal with imprecise or vague information contained in data sets [1]. The basic concept of rough set theory lies with the indiscernibility relation [4].…”

Section: Preliminary Concepts Of Rough Set Theorymentioning

confidence: 99%

“…At the same time, it may contain many irrelevant attributes and less important or even unwanted features which are redundant to represent the essential knowledge content in the information system. Thus, the intrinsic dimension of the dataset may be very small [1] [13]. It is of interest for many researchers to reduce the dimension of the original dataset.…”

Section: Introductionmentioning

confidence: 99%

“…2) Feature Extraction: where the original high dimensional data is mapped into a space with lower dimension [15]. Rough Set Theory (RST) is a mathematical approach or tool that can be effectively used in data analysis especially to deal with incomplete and uncertain data [1]. Reducts in rough set theory is the minimal set of attributes that hold the complete knowledge of the dataset [1] [15].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Method to Reduce Attributes from Reducts

Remesh¹

2019

IJRASET

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A major challenge in present day data mining is the high dimension of datasets. Because of the high dimensionality of data, feature selection becomes a necessary pre-processing step to data mining, which removes superfluous features and enhances performance of classification. Thus, attribute reduction becomes an important pre-processing step for data mining. The concepts of rough set theory have been successfully used for attribute reduction. Reducts in rough set theory provide a minimal set of attributes preserving the knowledge. Removal of an attribute from a reduct creates information loss. The severity of information loss due to removal of an attribute depends on the significance of that attribute. In many cases, the variation in classification accuracy when an attribute that has negligible significance is removed from a reduct may not be significant. Removal of insignificant attributes is useful when dealing with high dimensional data sets. In this paper, a novel method is proposed to identify attributes with negligible significance which can be removed from a reduct. Experimental results for various datasets are presented. The results prove that the accuracy of classification do not vary much after the removal of the insignificant attributes from the reduct.

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Section: Preliminary Concepts Of Rough Set Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Method to Reduce Attributes from Reducts

Remesh¹

2019

IJRASET

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“…So it is important to develop some techniques to reduce the dimensionality of mixture structure with loss of generality and technique of attribute reduction in rough set theory may play an important role in such type situations.Pawlak [12] introduce the concept of RST to deal with uncertain, incomplete or vague information. RST is easy to use since it does not require additional information such as probability distribution, a prior probability etc [2]. RST is an extension of set theory and has the implicit feature of compressing the dataset.…”

Section: Introductionmentioning

confidence: 99%

An Approach of Factor Screening in Multifactor Mixture Experiments using Rough Set Theory

2016

IJSR

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A large number of experiments are being conducted in industrial and other branches of science with an objective to compare and test quantity of inputs applied as mixture of two or more components. In particle, there are many situations where mixture experiments involve large number of factors, such types of mixture experiments are known as multifactor mixture experiments. Due to their practical applicability it is important to make such experiment user friendly. Reducing the number of factors without loss of information is an important step in this direction. Therefore, in present study rough set theory (RST), which is an extension of the set theory and has the implicit feature of compressing the dataset, is used to reduce the factors in the multifactor mixture experiments.

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One-to-one marketing management via customer complaint

Kunathikornkit

Piriyakul

2023

Soc. Netw. Anal. Min.

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For companies to retain customers and ensure effective management-level resolution, they need to anticipate customer churn and determine the root cause of complaints. To achieve this, analyzing personalized complaints from the customer's perspective is crucial. This research advocates for a multidisciplinary approach that combines language behavior, relevance feature extraction, feature weighting, and sentiment analysis to extract the underlying problem in real time. Applying this approach to the CFPB database sample yielded an accuracy rate of 82% and a system validity of 75%, which can help improve customer service and protect consumers in the nancial and other service industries. By addressing individual customer issues that cause dissatisfaction, businesses can enhance customer satisfaction and retention levels. Thus, by analyzing complaints from a personalized standpoint, companies can identify the root cause of the problem, improve their services, and establish stronger customer relationships.

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An Innovative Approach for Attribute Reduction in Rough Set Theory

Cited by 6 publications

References 13 publications

A Novel Method to Reduce Attributes from Reducts

A Novel Method to Reduce Attributes from Reducts

An Approach of Factor Screening in Multifactor Mixture Experiments using Rough Set Theory

One-to-one marketing management via customer complaint

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