Evaluation of Classification for Project Features with Machine Learning Algorithms
Due to the asymmetry of project features, it is difficult for project managers to make a reliable prediction of the decision-making process. Big data research can establish more predictions through the results of accurate classification. Machine learning (ML) has been widely applied for big data ana...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/2/372 |
| _version_ | 1797476424849817600 |
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| author | Ching-Lung Fan |
| author_facet | Ching-Lung Fan |
| author_sort | Ching-Lung Fan |
| collection | DOAJ |
| description | Due to the asymmetry of project features, it is difficult for project managers to make a reliable prediction of the decision-making process. Big data research can establish more predictions through the results of accurate classification. Machine learning (ML) has been widely applied for big data analytic and processing, which includes model symmetry/asymmetry of various prediction problems. The purpose of this study is to achieve symmetry in the developed decision-making solution based on the optimal classification results. Defects are important metrics of construction management performance. Accordingly, the use of suitable algorithms to comprehend the characteristics of these defects and train and test massive data on defects can conduct the effectual classification of project features. This research used 499 defective classes and related features from the Public Works Bid Management System (PWBMS). In this article, ML algorithms, such as support vector machine (SVM), artificial neural network (ANN), decision tree (DT), and Bayesian network (BN), were employed to predict the relationship between three target variables (engineering level, project cost, and construction progress) and defects. To formulate and subsequently cross-validate an optimal classification model, 1015 projects were considered in this work. Assessment indicators showed that the accuracy of ANN for classifying the engineering level is 93.20%, and the accuracy values of SVM for classifying the project cost and construction progress are 85.32% and 79.01%, respectively. In general, the SVM yielded better classification results from these project features. This research was based on an ML algorithm evaluation system for buildings as a classification model for project features with the goal of aiding project managers to comprehend defects. |
| first_indexed | 2024-03-09T20:57:38Z |
| format | Article |
| id | doaj.art-9c21f225737d412495098cdaef2fcd59 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T20:57:38Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-9c21f225737d412495098cdaef2fcd592023-11-23T22:17:30ZengMDPI AGSymmetry2073-89942022-02-0114237210.3390/sym14020372Evaluation of Classification for Project Features with Machine Learning AlgorithmsChing-Lung Fan0Department of Civil Engineering, The Republic of China Military Academy, No. 1, Weiwu Rd., Fengshan, Kaohsiung 830, TaiwanDue to the asymmetry of project features, it is difficult for project managers to make a reliable prediction of the decision-making process. Big data research can establish more predictions through the results of accurate classification. Machine learning (ML) has been widely applied for big data analytic and processing, which includes model symmetry/asymmetry of various prediction problems. The purpose of this study is to achieve symmetry in the developed decision-making solution based on the optimal classification results. Defects are important metrics of construction management performance. Accordingly, the use of suitable algorithms to comprehend the characteristics of these defects and train and test massive data on defects can conduct the effectual classification of project features. This research used 499 defective classes and related features from the Public Works Bid Management System (PWBMS). In this article, ML algorithms, such as support vector machine (SVM), artificial neural network (ANN), decision tree (DT), and Bayesian network (BN), were employed to predict the relationship between three target variables (engineering level, project cost, and construction progress) and defects. To formulate and subsequently cross-validate an optimal classification model, 1015 projects were considered in this work. Assessment indicators showed that the accuracy of ANN for classifying the engineering level is 93.20%, and the accuracy values of SVM for classifying the project cost and construction progress are 85.32% and 79.01%, respectively. In general, the SVM yielded better classification results from these project features. This research was based on an ML algorithm evaluation system for buildings as a classification model for project features with the goal of aiding project managers to comprehend defects.https://www.mdpi.com/2073-8994/14/2/372support vector machinesartificial neural networkdecision treesBayesian networkmachine learningdefects |
| spellingShingle | Ching-Lung Fan Evaluation of Classification for Project Features with Machine Learning Algorithms Symmetry support vector machines artificial neural network decision trees Bayesian network machine learning defects |
| title | Evaluation of Classification for Project Features with Machine Learning Algorithms |
| title_full | Evaluation of Classification for Project Features with Machine Learning Algorithms |
| title_fullStr | Evaluation of Classification for Project Features with Machine Learning Algorithms |
| title_full_unstemmed | Evaluation of Classification for Project Features with Machine Learning Algorithms |
| title_short | Evaluation of Classification for Project Features with Machine Learning Algorithms |
| title_sort | evaluation of classification for project features with machine learning algorithms |
| topic | support vector machines artificial neural network decision trees Bayesian network machine learning defects |
| url | https://www.mdpi.com/2073-8994/14/2/372 |
| work_keys_str_mv | AT chinglungfan evaluationofclassificationforprojectfeatureswithmachinelearningalgorithms |