A rough set-based case-based reasoner for text categorization

Li, Y.; Shiu, Simon; Pal, Sankar K.; Liu, J. N. K.

doi:10.1016/j.ijar.2005.06.019

Cited by 39 publications

(11 citation statements)

References 24 publications

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“…In this section, we provide a number of rough set methods that can be used in construction of classifiers. For more information the reader is referred, e.g., to [2,10,17,18,22,24,26,[29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][53][54][55][56][58][59][60]64,65,67,76,3,[83][84][85][86][87][88]93,[97][98][99][100][101][102][103]…”

Section: Rough Set Methods For Machine Learning Pattern Recognitionmentioning

confidence: 99%

Rough sets and Boolean reasoning

Pawlak

Skowron

2007

Information Sciences

847

306

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Section: Rough Set Methods For Machine Learning Pattern Recognitionmentioning

confidence: 99%

Rough sets and Boolean reasoning

Pawlak

Skowron

2007

Information Sciences

847

306

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“…Some relationships have already been established between rough sets and other approaches as well as a wide range of hybrid systems have been developed (see, e.g., [8,37,60,39,40,72,46,48,59,66,67,96,128,103,127,134,136,139,148,149,157,[189][190][191][192][193][194][195][196]207,208,216,219,229,230,232,236,244,245,251,265,188,279,281,286,288,295,299,302,317,…”

Section: Exemplary Research Directions and Applicationsmentioning

confidence: 99%

Rudiments of rough sets

Pawlak

Skowron

2007

Information Sciences

1,850

791

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“…They proposed a semi-structured format for case representation using the Zachman framework and presented an efficient approach to reasoning similar cases for decision-making. Li et al (2006) presented a novel rough set-based case-based reasoner for application of text categorization. Jiang et al (2006) presented a novel methodology for utilizing a fuzzy similarity-based Rough Set algorithm in feature weighting and reduction for CBR systems in tool selection for die and mold NC machining.…”

Section: Introductionmentioning

confidence: 99%

A combined data mining approach using rough set theory and case-based reasoning in medical datasets

Rezvan¹,

Hamadani²,

Saffari³

et al. 2014

10.5267/j.dsl

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Case-based reasoning (CBR) is the process of solving new cases by retrieving the most relevant ones from an existing knowledge-base. Since, irrelevant or redundant features not only remarkably increase memory requirements but also the time complexity of the case retrieval, reducing the number of dimensions is an issue worth considering. This paper uses rough set theory (RST) in order to reduce the number of dimensions in a CBR classifier with the aim of increasing accuracy and efficiency. CBR exploits a distance based co-occurrence of categorical data to measure similarity of cases. This distance is based on the proportional distribution of different categorical values of features. The weight used for a feature is the average of cooccurrence values of the features. The combination of RST and CBR has been applied to real categorical datasets of Wisconsin Breast Cancer, Lymphography, and Primary cancer. The 5-fold cross validation method is used to evaluate the performance of the proposed approach. The results show that this combined approach lowers computational costs and improves performance metrics including accuracy and interpretability compared to other approaches developed in the literature.

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A rough set-based case-based reasoner for text categorization

Cited by 39 publications

References 24 publications

Rough sets and Boolean reasoning

Rough sets and Boolean reasoning

Rudiments of rough sets

A combined data mining approach using rough set theory and case-based reasoning in medical datasets

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