A Novel Data Filling Algorithm for Incomplete Information System Based on Valued Limited Tolerance Relation

Bai, Xiuling; Zhang, Mingchuan; Wu, Qingtao; Zheng, Ruijuan; Zhao, Heng; Wei, Wangyang

doi:10.14257/ijdta.2015.8.6.14

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…(c) Rough set theory, which deals with four kinds of missing attributes values: lost values (the values that were recorded but currently are unavailable), "do not care" conditions (the original values were irrelevant), restricted "do not care" conditions (similar to ordinary "do not care" conditions but interpreted differently), and attribute-concept values (these missing attribute values may be replaced by any attribute value limited to the same concept) (Stefanowski and Tsoukias, 2001;Grzymala-Busse, 2006;Bai et al, 2015). Clark et al (2013) use rough sets theory to deal with consistency of incomplete data sets (a data set is defined as consistent when any pair of samples with the same attribute values belongs to the same class); they discuss two types of missing attribute values: lost values and "do not care" conditions.…”

Section: Related Workmentioning

confidence: 99%

Lookahead selective sampling for incomplete data

Abdallah

Shimshoni

2016

International Journal of Applied Mathematics and Computer Science

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Missing values in data are common in real world applications. There are several methods that deal with this problem. In this paper we present lookahead selective sampling (LSS) algorithms for datasets with missing values. We developed two versions of selective sampling. The first one integrates a distance function that can measure the similarity between pairs of incomplete points within the framework of the LSS algorithm. The second algorithm uses ensemble clustering in order to represent the data in a cluster matrix without missing values and then run the LSS algorithm based on the ensemble clustering instance space (LSS-EC). To construct the cluster matrix, we use the k-means and mean shift clustering algorithms especially modified to deal with incomplete datasets. We tested our algorithms on six standard numerical datasets from different fields. On these datasets we simulated missing values and compared the performance of the LSS and LSS-EC algorithms for incomplete data to two other basic methods. Our experiments show that the suggested selective sampling algorithms outperform the other methods.

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Section: Related Workmentioning

confidence: 99%

Lookahead selective sampling for incomplete data

Abdallah

Shimshoni

2016

International Journal of Applied Mathematics and Computer Science

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Dealing with Missing Data using a Selection Algorithm on Rough Sets

Prieto-Cubides¹,

Argoty²

2018

IJCIS

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This paper discusses the so-called missing data problem, i.e. the problem of imputing missing values in information systems. A new algorithm, called the ARSI algorithm, is proposed to address the imputation problem of missing values on categorical databases using the framework of rough set theory. This algorithm can be seen as a refinement of the ROUSTIDA algorithm and combines the approach of a generalized non-symmetric similarity relation with a generalized discernibility matrix to predict the missing values on incomplete information systems. Computational experiments show that the proposed algorithm is as efficient and competitive as other imputation algorithms.

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A Novel Data Filling Algorithm for Incomplete Information System Based on Valued Limited Tolerance Relation

Abstract: Abstract

Cited by 2 publications

References 8 publications

Lookahead selective sampling for incomplete data

Lookahead selective sampling for incomplete data

Dealing with Missing Data using a Selection Algorithm on Rough Sets

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