Rough Set Model in Incomplete Decision Systems

Cao, Thinh; Yamada, Kôichi; Unehara, Muneyuki; Nguyen, Do Van

doi:10.20965/jaciii.2017.p1221

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…Hu and Yao proposed structured rough set approximation in complete and incomplete information systems to serve as a basis of three-way decisions with rough set [5]. To deal with an incomplete information system, a more generalized approach that considered potential candidates was presented [6].…”

Section: Introductionmentioning

confidence: 99%

Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection

Zhao

Zhu

2019

Applied Sciences

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With the rapid development of the network, data fusion becomes an important research hotspot. Large amounts of data need to be preprocessed in data fusion; in practice, the features of datasets can be filtered to reduce the amount of data. The feature selection based on fuzzy rough sets can process a large number of continuous and discrete data to reduce the data dimension, making the selected feature subset highly correlated with the classification but less dependent on other features. In this paper, a new method of fuzzy rough feature selection is proposed which combines the membership function determination method of fuzzy c-means clustering and fuzzy equivalence to the original selection. Different from the existing research, our method takes full advantage of knowledge about the dataset itself and the differences between datasets, which makes the features selected have a higher correlation with the classification, improves the classification accuracy, and reduces the data dimension. Experimental results on the UCI machine learning repository datasets confirmed the performance and effectiveness of our method. Compared to the existing method, smaller subsets of features and an average of 1% higher classification accuracies were achieved.

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Section: Introductionmentioning

confidence: 99%

Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection

Zhao

Zhu

2019

Applied Sciences

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“…A common approach might be to remove such objects with missing decision data. However, in our previous work [22,23], we gave evidence that removing such objects may lead to information loss. For example, the removal may change the original data distribution, break relations between condition attributes, or the induced knowledge after the removal would be different from the knowledge in the original…”

Section: Introductionmentioning

confidence: 84%

“…A common approach to deal with such objects with missing decision data is to remove them. In our previous studies, however, we proved that removing such objects may lead to information loss [22,23]. Hence, instead of removing such objects, we proposed a parameter-based method to induce knowledge from such information system.…”

Section: Literature Reviewmentioning

confidence: 97%

Parallel Computation of Rough Set Approximations in Information Systems with Missing Decision Data

et al. 2018

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The paper discusses the use of parallel computation to obtain rough set approximations from large-scale information systems where missing data exist in both condition and decision attributes. To date, many studies have focused on missing condition data, but very few have accounted for missing decision data, especially in enlarging datasets. One of the approaches for dealing with missing data in condition attributes is named twofold rough approximations. The paper aims to extend the approach to deal with missing data in the decision attribute. In addition, computing twofold rough approximations is very intensive, thus the approach is not suitable when input datasets are large. We propose parallel algorithms to compute twofold rough approximations in large-scale datasets. Our method is based on MapReduce, a distributed programming model for processing large-scale data. We introduce the original sequential algorithm first and then the parallel version is introduced. Comparison between the two approaches through experiments shows that our proposed parallel algorithms are suitable for and perform efficiently on large-scale datasets that have missing data in condition and decision attributes.

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“…All the above attempts have one thing in common-they impute missing values in collected data. This imputation is harmful because it affects the data distribution, on the one hand, and breaks down some relations between conditional features and the decision label, on the other [27]. Besides, the above attempts do not handle heterogeneous data directly.…”

Section: Related Workmentioning

confidence: 99%

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm

Hamed

Sobhy

Nassar

2020

Preprint

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The coronavirus 2019 disease (COVID-19) is wreaking havoc around the world, and great efforts are underway to control it. Millions of people are now being tested and their data keeps accumulating in large volumes. This data can be used to classify newly tested persons as whether they have the disease or not. However, normal classification techniques are hampered by the fact that the data is typically both incomplete and heterogeneous. To address this two-fold obstacle, we propose a KNN variant (KNNV) algorithm which accurately and efficiently classifies COVID-19. The main two ideas behind the proposed algorithm are that for each instance to be classified it chooses the parameter K adaptively and calculates the distances to other instances in a novel way. The KNNV was implemented and tested on a COVID-19 dataset from the Italian society of medical and intervention radiology society. It was also compared to three algorithms of its category. The test results show that the KNNV can efficiently and accurately classify COVID-19 patients. The comparison results show that the algorithm greatly outperforms all its competitors in terms of four metrics: precision, recall, accuracy, and F-Score.

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Rough Set Model in Incomplete Decision Systems

Cited by 5 publications

References 25 publications

Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection

Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection

Parallel Computation of Rough Set Approximations in Information Systems with Missing Decision Data

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm

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