Learning coverage rules from incomplete data based on rough sets

Hong, Tzung-Pei; Tseng, Li-Huei; Chien, Been-Chian

doi:10.1109/icsmc.2004.1400837

Cited by 6 publications

(1 citation statement)

References 20 publications

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“…According to study, the idea of attribute-value pair block is used to determine characteristic sets, characteristic relations, lower and upper approximation and rule extraction from an incomplete decision table. Some other rough set approaches to handling missing attribute values are presented in Hong, Tseng and Chien (2004), Nakata and Sakai (2005); Wang (2002) as well. Nguyen (2008) proposed a method of Boolean reasoning and discernibility for efficient computation of different entities like reduct and rule generation.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study Based on Rough Set and Classification Via Clustering Approaches to Handle Incomplete Data to Predict Learning Styles

Rana

Lal

2017

International Journal of Decision Support System Technology

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Handling of missing attribute values are a big challenge for data analysis. For handling this type of problems, there are some well known approaches, including Rough Set Theory (RST) and classification via clustering. In the work reported here, RSES (Rough Set Exploration System) one of the tools based on RST approach, and WEKA (Waikato Environment for Knowledge Analysis), a data mining tool-based on classification via clustering-are used for predicting learning styles from given data, which possibly has missing values. The results of the experiments using the tools show that the problem of missing attribute values is better handled by RST approach as compared to the classification via clustering approach. Further, in respect of missing values, RSES yields better decision rules, if the missing values are simply ignored than the rules obtained by assigning some values in place of missing attribute values.

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Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study Based on Rough Set and Classification Via Clustering Approaches to Handle Incomplete Data to Predict Learning Styles

Rana

Lal

2017

International Journal of Decision Support System Technology

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Mining data with numerical attributes and missing attribute values — A rough set approach

Grzymała-Busse

Hippe

2011

2011 IEEE International Conference on Granular Computing

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A valued tolerance approach to missing attribute values in data mining

Grzymała-Busse

Hippe

Rząsa

et al. 2009

2009 2nd Conference on Human System Interactions

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One of the newest approaches to missing attribute values in data sets is based on a valued tolerance relation. The valued tolerance relation method of handling missing attribute values was not yet experimentally compared with other methods. The main objective of this paper was to compare the quality of two methods handling missing attribute values, one of them was the valued tolerance method, the other method was the MLEM2 approach, using the same interpretation of missing attribute values but a different approach to computing approximations and rule induction. Both methods were compared using not only an error rate, a result of ten-fold cross validation, but also complexity of induced rule sets.Our conclusion is that neither of these two methods is better in terms of the error rate. However, the MLEM2 approach produces, in most cases, less complex rule sets than the valued tolerance method.

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Learning coverage rules from incomplete data based on rough sets

Cited by 6 publications

References 20 publications

A Comparative Study Based on Rough Set and Classification Via Clustering Approaches to Handle Incomplete Data to Predict Learning Styles

A Comparative Study Based on Rough Set and Classification Via Clustering Approaches to Handle Incomplete Data to Predict Learning Styles

Mining data with numerical attributes and missing attribute values — A rough set approach

A valued tolerance approach to missing attribute values in data mining

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Learning coverage rules from incomplete data based on rough sets

Cited by 6 publications

References 20 publications

A Comparative Study Based on Rough Set and Classification Via Clustering Approaches to Handle Incomplete Data to Predict Learning Styles

A Comparative Study Based on Rough Set and Classification Via Clustering Approaches to Handle Incomplete Data to Predict Learning Styles

Mining data with numerical attributes and missing attribute values &#x2014; A rough set approach

A valued tolerance approach to missing attribute values in data mining

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Product

Resources

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Mining data with numerical attributes and missing attribute values — A rough set approach