Prediction of compressive strength class of concrete with dominance based rough set approach*

Ahmet Topal

Makale detayları

Prediction of compressive strength class of concrete with dominance based rough set approach*

Yazarlar:

Ahmet Topal

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Clinical Chemistry

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.41 Issue 6, pp.1088-1095

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001311381

pISSN:

1304-7191

Anahtar kelimeler:

Özet:

 Dominance based rough set approach is important in studies conducted with datasets con-taining uncertainty. In this study, a dataset consisting of 1030 samples obtained in the laboratory regarding compressive strength of concrete has been considered. The decision attribute, which has continuous values, has been made discrete for applying dominance relation. In order to measure performance, samples in the dataset have been divided into two groups: the training set and the testing set. This process has been done in a way that corresponds to the distribution of each class within the dataset. On the other hand, since there is a class which has more or less samples than the others, synthetic data generation has been done with Synthetic Minority Oversampling Technique SMOTE in order to handle the between-class imbalance problem and equalize the number of samples in the classes. As a result, the training set has been made perfectly balanced. A decision-support model which extracts if then exact decision rules has been designed to be used in determining the quality or compressive strength of the concrete samples by using dominance based rough set approach. Performance of these rules on the testing set through the confusion matrix has been discussed. The exper-imental results show that performance of the exact decision rules induced by the dominance rough set approach on the testing set is significant.

 Dominance based rough set approach is important in studies conducted with datasets con-taining uncertainty. In this study, a dataset consisting of 1030 samples obtained in the laboratory regarding compressive strength of concrete has been considered. The decision attribute, which has continuous values, has been made discrete for applying dominance relation. In order to measure performance, samples in the dataset have been divided into two groups: the training set and the testing set. This process has been done in a way that corresponds to the distribution of each class within the dataset. On the other hand, since there is a class which has more or less samples than the others, synthetic data generation has been done with Synthetic Minority Oversampling Technique SMOTE in order to handle the between-class imbalance problem and equalize the number of samples in the classes. As a result, the training set has been made perfectly balanced. A decision-support model which extracts if then exact decision rules has been designed to be used in determining the quality or compressive strength of the concrete samples by using dominance based rough set approach. Performance of these rules on the testing set through the confusion matrix has been discussed. The exper-imental results show that performance of the exact decision rules induced by the dominance rough set approach on the testing set is significant.

 Dominance based rough set approach is important in studies conducted with datasets con-taining uncertainty. In this study, a dataset consisting of 1030 samples obtained in the laboratory regarding compressive strength of concrete has been considered. The decision attribute, which has continuous values, has been made discrete for applying dominance relation. In order to measure performance, samples in the dataset have been divided into two groups: the training set and the testing set. This process has been done in a way that corresponds to the distribution of each class within the dataset. On the other hand, since there is a class which has more or less samples than the others, synthetic data generation has been done with Synthetic Minority Oversampling Technique SMOTE in order to handle the between-class imbalance problem and equalize the number of samples in the classes. As a result, the training set has been made perfectly balanced. A decision-support model which extracts if then exact decision rules has been designed to be used in determining the quality or compressive strength of the concrete samples by using dominance based rough set approach. Performance of these rules on the testing set through the confusion matrix has been discussed. The exper-imental results show that performance of the exact decision rules induced by the dominance rough set approach on the testing set is significant.

 Dominance based rough set approach is important in studies conducted with datasets con-taining uncertainty. In this study, a dataset consisting of 1030 samples obtained in the laboratory regarding compressive strength of concrete has been considered. The decision attribute, which has continuous values, has been made discrete for applying dominance relation. In order to measure performance, samples in the dataset have been divided into two groups: the training set and the testing set. This process has been done in a way that corresponds to the distribution of each class within the dataset. On the other hand, since there is a class which has more or less samples than the others, synthetic data generation has been done with Synthetic Minority Oversampling Technique SMOTE in order to handle the between-class imbalance problem and equalize the number of samples in the classes. As a result, the training set has been made perfectly balanced. A decision-support model which extracts if then exact decision rules has been designed to be used in determining the quality or compressive strength of the concrete samples by using dominance based rough set approach. Performance of these rules on the testing set through the confusion matrix has been discussed. The exper-imental results show that performance of the exact decision rules induced by the dominance rough set approach on the testing set is significant.

Makale detayları

Prediction of compressive strength class of concrete with dominance based rough set approach*

Yazarlar:

Ahmet Topal

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Clinical Chemistry

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.41 Issue 6, pp.1088-1095

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001311381

pISSN:

1304-7191

Dominance Based Rough Set

Decision Rules

Compressive Strength Of Concrete

Decision- Support Model

Sayı

Makale detayları

Prediction of compressive strength class of concrete with dominance based rough set approach*

Yazarlar: Ahmet Topal

Yayıncı: Yildiz Technical University

Ana Başlıklar: Clinical Chemistry

Dergi: Sigma Journal of Engineering and Natural Sciences Vol.41 Issue 6, pp.1088-1095

Belge Tipi: Makale

Yayın Tarihi: 12/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001311381

pISSN: 1304-7191

Dominance Based Rough Set

Decision Rules

Compressive Strength Of Concrete

Decision- Support Model

Sayı

Yazarlar:

Ahmet Topal

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Clinical Chemistry

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.41 Issue 6, pp.1088-1095

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001311381

pISSN:

1304-7191