Diverse classifiers ensemble based on GMDH-type neural network algorithm for binary classification

Dağ, Osman; Kasikci, Merve; Karabulut, Erdem; Alpar, Reha

doi:10.1080/03610918.2019.1697451

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…The GMDH has been successfully used for computer-based mathematical modeling in complex systems, as well as data mining, optimization, and pattern recognition problems [72]. The process of GMDH is similar to a type of self-organizing network [73,74]. The mapping between the input and output variables in a GMDH neural network is a nonlinear function.…”

Section: Group Methods Of Data Handling-type Neural Networkmentioning

confidence: 99%

“…Finally, in the convergence process of the algorithm, if the results in the layer (n + 1) are better than the layer (n), then the algorithm converges. Equations ( 1) and ( 2) indicate the relationship between the approximate function ( ∧ f ) with the multi-input and single-output ( ∧ y) dataset and the least possible error between actual and predicted values [73,74].…”

Section: Group Methods Of Data Handling-type Neural Networkmentioning

confidence: 99%

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Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido

Haghshenas

et al. 2022

Safety

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The evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, the evaluation and analysis of important contributing factors affecting the number of vehicles involved in crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms, including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA), are employed. Hence, the number of vehicles involved in an accident is considered to be the output, and the seven factors affecting transport safety, including Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS), and Annual average daily traffic (AADT) of rural roads in Cosenza, southern Italy, are selected as the inputs. In this study, 564 data sets from rural areas were investigated, and the relevant, effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation in rural areas. The results obtained demonstrated that the “Type of accident” has the highest level and “Location” has the lowest importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

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Section: Group Methods Of Data Handling-type Neural Networkmentioning

confidence: 99%

Section: Group Methods Of Data Handling-type Neural Networkmentioning

confidence: 99%

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido

Haghshenas

et al. 2022

Safety

View full text Add to dashboard Cite

show abstract

“…Finally, in the convergence process of the algorithm, if the results in the layer (n+1) are better than the layer (n), then the algorithm converges. Eqs 1 and 2 indicate the relationship among the approximate function ( f ) with the multi-input and single-output ( y  ) dataset and the least possible error between actual and predicted values [70][71].…”

Section: Group Methods Of Data Handling-type Neural Networkmentioning

confidence: 99%

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido¹,

Haghshenas²,

Haghshenas³

et al. 2021

Preprint

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Evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, evaluation and analysis of important contributing factors affecting the number of crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA) are employed for evaluating the number of vehicles involved in the accident based on the seven factors affecting transport safety including the Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS) and Annual average daily traffic (AADT) of rural roads of Cosenza in southern Italy. In this study, 564 data sets of rural areas were investigated and relevant effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation for rural areas. The results obtained demonstrated that "Average speed" has the highest level and "Weekday" has the lowest level of importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

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“…The models are based upon the dataset and they are self-regulating models. Equation (7) shows the general form of the GMDH basic neural network map according to input and output data [45]:…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Development of a Binary Model for Evaluating Water Distribution Systems by a Pressure Driven Analysis (PDA) Approach

Morosini

Haghshenas

et al. 2020

Applied Sciences

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Investigation of Water Distribution Networks (WDNs) is considered a challenging task due to the unpredicted and uncertain conditions in water engineering. When in a WDN, a pipe failure occurs, and shut-off valves to isolate the broken pipe to allow repairing works are activated. In these new conditions, the hydraulic parameters in the network are modified because the topology of the entire system changes. If the head becomes inadequate, the Pressure Driven Analysis (PDA) is the correct approach to evaluate the performance of water networks. Hence, in the present study, the water distribution system was evaluated in pressure-driven conditions for 100 different scenarios and then using a type of neural network called Group Method of Data Handling (GMDH) as a stochastic technique. For this purpose, several most notable parameters including the base demand, pressure, and alpha (the percentage of effective supplied flow) were calculated using simulations based on a PDA approach and applied to the water distribution network of Praia a Mare in Southern Italy. In the second stage, the output parameters were used in a developed binary classification model. Finally, the obtained results showed that the GMDH algorithm can be applied as a powerful tool for modeling water distribution networks.

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Diverse classifiers ensemble based on GMDH-type neural network algorithm for binary classification

Cited by 7 publications

References 21 publications

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Development of a Binary Model for Evaluating Water Distribution Systems by a Pressure Driven Analysis (PDA) Approach

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