Predicting ambulance offload delay using a hybrid decision tree model

Li, Mengyu; Vanberkel, Peter T.; Zhong, Xiang

doi:10.1016/j.seps.2021.101146

Cited by 18 publications

(13 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first tree-based model used in this study is the decision tree. As one of the basic methods for dealing with regression and classification problems [ 35 ], the decision tree conducts regression and classification tasks by variables with continuous and discrete values, respectively [ 36 ]. This study used decision trees for regression problems.…”

Section: Tree-based Machine Learning Architecture To Predict Impedanc...mentioning

confidence: 99%

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

Lai

Lin

et al. 2023

Micromachines

View full text Add to dashboard Cite

The transmission characteristics of the printed circuit board (PCB) ensure signal integrity and support the entire circuit system, with impedance matching being critical in the design of high-speed PCB circuits. Because the factors affecting impedance are closely related to the PCB production process, circuit designers and manufacturers must work together to adjust the target impedance to maintain signal integrity. Five machine learning models, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and light gradient boosting machine (LightGBM), were used to forecast target impedance values. Furthermore, the Optuna algorithm is used to determine forecasting model hyperparameters. This study applied tree-based machine learning techniques with Optuna to predict impedance. The results revealed that five tree-based machine learning models with Optuna can generate satisfying forecasting accuracy in terms of three measurements, including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2). Meanwhile, the LightGBM model with Optuna outperformed the other models. In addition, by using Optuna to tune the parameters of machine learning models, the accuracy of impedance matching can be increased. Thus, the results of this study suggest that the tree-based machine learning techniques with Optuna are a viable and promising alternative for predicting impedance values for circuit analysis.

show abstract

Section: Tree-based Machine Learning Architecture To Predict Impedanc...mentioning

confidence: 99%

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

Lai

Lin

et al. 2023

Micromachines

View full text Add to dashboard Cite

show abstract

“…Furthermore, decision trees can handle data of different types without requiring any transformation of the data. Most importantly, decision trees have the capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution that is often easier to interpret [ 14 ]. Moreover, ANN and KNN were used for the evaluation of the Ga rank&rand accuracy.…”

Section: Experimental Studymentioning

confidence: 99%

Hybrid feature selection based on SLI and genetic algorithm for microarray datasets

et al. 2022

View full text Add to dashboard Cite

One of the major problems in microarray datasets is the large number of features, which causes the issue of “the curse of dimensionality” when machine learning is applied to these datasets. Feature selection refers to the process of finding optimal feature set by removing irrelevant and redundant features. It has a significant role in pattern recognition, classification, and machine learning. In this study, a new and efficient hybrid feature selection method, called Ga rank&rand , is presented. The method combines a wrapper feature selection algorithm based on the genetic algorithm (GA) with a proposed filter feature selection method, SLI- γ . In Ga rank&rand , some initial solutions are built regarding the most relevant features based on SLI- γ , and the remaining ones are only the random features. Eleven high-dimensional and standard datasets were used for the accuracy evaluation of the proposed SLI- γ . Additionally, four high-dimensional well-known datasets of microarray experiments were used to carry out an extensive experimental study for the performance evaluation of Ga rank&rand . This experimental analysis showed the robustness of the method as well as its ability to obtain highly accurate solutions at the earlier stages of the GA evolutionary process. Finally, the performance of Ga rank&rand was also compared to the results of GA to highlight its competitiveness and its ability to successfully reduce the original feature set size and execution time.

show abstract

“…Making a Decision Tree model [38] [39] is like drawing an inverted tree where the Root Node is in the top position. Internal Node that has 1 input and at least 2 outputs.…”

Section: F Decision Treementioning

confidence: 99%

Android APK Identification using Non Neural Network and Neural Network Classifier

Hindarto

Santoso²

2021

J-Cosine

View full text Add to dashboard Cite

Currently adoption of mobile phones and mobile applications based on Android operating system is increasing rapidly. Many companies and emerging startups are carrying out digital transformation by using mobile applications to provide disruptive digital services to replace existing old styled services. This transformation prompted the attackers to create malicious software (malware) using sophisticate methods to target victims of Android mobile phone users. The purpose of this study is to identify Android APK files by classifying them using Artificial Neural Network (ANN) and Non Neural Network (NNN). The ANN is Multi-Layer Perceptron Classifier (MLPC), while the NNN are KNN, SVM, Decision Tree, Logistic Regression and Naïve Bayes methods. The results show that the performance using NNN has decreasing accuracy when training using larger datasets. The use of the K-Nearest Neighbor algorithm with a dataset of 600 APKs achieves an accuracy of 91.2% and dataset of 14170 APKs achieves an accuracy of 88%. The using of the Support Vector Machine algorithm with the 600 APK dataset has an accuracy of 99.1% and the 14170 APK dataset has an accuracy of 90.5%. The using of the Decision Tree algorithm with the 600 APK dataset has an accuracy of 99.2%, the 14170 APK dataset has an accuracy of 90.8%. The experiment using the Multi-Layer Perceptron Classifier has increasing with the 600 APK dataset reaching 99%, the 7000 APK dataset reaching 100% and the 14170 APK dataset reaching 100%.

show abstract

Predicting ambulance offload delay using a hybrid decision tree model

Cited by 18 publications

References 46 publications

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

Hybrid feature selection based on SLI and genetic algorithm for microarray datasets

Android APK Identification using Non Neural Network and Neural Network Classifier

Contact Info

Product

Resources

About