Development of a decision support model for determining the target multi-family housing complex for green remodeling using data mining techniques

Jeong, Kwangbok; Hong, Taehoon; Chae, Myeongsoo; Kim, Jimin

doi:10.1016/j.enbuild.2019.109401

Cited by 12 publications

(5 citation statements)

References 28 publications

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“…Chiranjeevi and Ranjana [4] A decision support model was developed for determining the target multi-family housing complex (MFHC) for green remodeling using a data mining technique. Jeong et al [5] locate the goal of MFHC for green remodel that is necessary to establish a careful and sensible evaluation method of the building energy performance. The energy benchmark for MFHC in South Korea, but there was a limitation that the study was conducted on the MFHC used district heating system.…”

Section: Methods In Literaturementioning

confidence: 99%

See 1 more Smart Citation

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Ba’abbad¹,

Althubiti²,

Alharbi³

et al. 2021

JDAIP

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Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students' records to predict degree completion time.

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Section: Methods In Literaturementioning

confidence: 99%

“…Data mining technique is used to enhance the building energy performance through determining the target multi-family housing complex (MFHC) for green remodeling [5]. In crime, preventing offense and force against the human female is one of the important goals.…”

Section: Introductionmentioning

confidence: 99%

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Ba’abbad¹,

Althubiti²,

Alharbi³

et al. 2021

JDAIP

View full text Add to dashboard Cite

show abstract

“…A decision support model was created for choosing the target multi-family housing complex (MFHC) for green remodeling using a data mining technique. Jeong et al [5] locate the target of MFHC for green redesign that is important to establish a careful and intelligent evaluation approach of the building energy performance. The energy benchmark for MFHC in South Korea, but there was a drawback that the study was conducted on the MFHC used district heating system.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Data mining approach is utilized to optimize the building energy performance through choosing the target multi-family housing complex (MFHC) for green remodeling [5]. In crime, preventing offense and force against the human female is one of the essential goals.…”

Section: Introductionmentioning

confidence: 99%

A Brief Overview of the Accuracy of Classification Algorithms for Data Prediction in Machine Learning Applications

Jen

2021

J. Appl. Data Sci.

View full text Add to dashboard Cite

Many business applications rely on their history data to anticipate their company future. The marketing products process is one of the essential procedures for the firm. Customer needs supply a useful piece of information that helps to promote the suitable products at the proper moment. Moreover, services are recognized recently as products. The development of education and health services is reliant on historical data. For the more, lowering online social media networks problems and crimes need a big supply of information. Data analysts need to utilize an efficient categorization system to predict the future of such businesses. However, dealing with a vast quantity of data demands tremendous time to process. Data mining encompasses numerous valuable techniques that are used to anticipate statistical data in a number of business applications. The classification technique is one of the most extensively utilized with a range of algorithms. In this work, numerous categorization methods are revised in terms of accuracy in diverse domains of data mining applications. A complete analysis is done following delegated reading of 20 papers in the literature. This study intends to allow data analysts to identify the best suitable classification algorithm for numerous commercial applications including business in general, online social media networks, agriculture, health, and education. Results reveal FFBPN is the best accurate algorithm in the business arena. The Random Forest algorithm is the most accurate in categorizing online social networks (OSN) activity. Naïve Bayes method is the most accurate to classify agriculture datasets. OneR is the most accurate method to classify occurrences inside the health domain. The C4.5 Decision Tree method is the most accurate to classify students' records to forecast degree completion time.

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“…Decision trees can provide comprehensive recommendations for optimizing multi-objective building designs. However, the application of decision trees in the field of architecture is primarily focused on areas such as building energy management [36], operations and maintenance [37], building code assessments [38], sociological analyses of buildings [39], and government recommendations [40]. Researchers in these areas typically play a primary role in building management and operations rather than in building design.…”

Section: Introductionmentioning

confidence: 99%

Research on the Decision-Making Method for the Passive Design Parameters of Zero Energy Houses in Severe Cold Regions Based on Decision Trees

Yao,

Chen,

Han

et al. 2024

Energies

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As the field of zero energy building design and research continues to progress, the use of data analysis methods is on the rise. These methods are applied to create assessment criteria, compare performance, and aid in design decision making. Decision trees, as a data-driven approach, offer interpretability and predictability, assisting designers in summarizing their design experience and serving as a foundation for design references. However, the current application of decision tree methods in the zero energy house sector primarily focuses on HVAC systems, lacking a comprehensive exploration from an architectural design perspective. Therefore, this study presents an empirical method for building and applying models based on decision trees, using zero energy house cases in severely cold regions of China as samples. Through an analysis of the interactions among various passive design parameters and the use of EnergyPlus for performance simulations, a decision tree model is established. This model aids in determining the recommended combinations of passive design parameters that meet the criteria of low energy consumption. Moreover, feature weighting highlights the most influential passive design parameters on building energy consumption, including the length of the architectural gestalt plane, the roof shape, and the ground thermal resistance. This research provides valuable methods and guidance for the design and construction of zero energy houses in severely cold regions of China.

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Development of a decision support model for determining the target multi-family housing complex for green remodeling using data mining techniques

Cited by 12 publications

References 28 publications

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

A Brief Overview of the Accuracy of Classification Algorithms for Data Prediction in Machine Learning Applications

Research on the Decision-Making Method for the Passive Design Parameters of Zero Energy Houses in Severe Cold Regions Based on Decision Trees

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