Search Results - (( variable selection tree algorithm ) OR ( java application optimisation algorithm ))

Application of Decision Trees in Athlete Selection: A Cart Algorithm Approach by Riska Wahyu, Romadhonia, A'yunin, Sofro, Danang, Ariyanto, Dimas Avian, Maulana, Junaidi Budi, Prihanto

“…The focus of this study is on the use of DTs, employing the Classification and Regression Trees (CART) algorithm, in the initial screening of athletes. …”
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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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Determination of tree stem volume : A case study of Cinnamomum by Noraini Abdullah

“…A modeling approach is developed which focuses on the phases in the model-building procedures, effects of interactions variables on the model, minimizing the effects of multicollinearity on the variables and recommending remedial techniques to overcome them, identification of the significant variables by removing insignificant variables, selecting the best model using the eight selection criteria (8SCs), and finally using the residual analysis to validate the chosen best model. …”
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Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear reg... by Balogun, A.-L., Tella, A.

“…The four machine learning algorithms exhibit high predictive performances, generally ascertaining the predictive accuracy of the climatic variables. …”
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Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear reg... by Balogun, A.-L., Tella, A.

“…The four machine learning algorithms exhibit high predictive performances, generally ascertaining the predictive accuracy of the climatic variables. …”
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Improved random forest for feature selection in writer identification by Sukor, Nooraziera Akmal

“…An algorithm and framework of Improved Random Forest (IRF) tree was applied for feature selection process. …”
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Decision tree and rule-based classification for predicting online purchase behavior in Malaysia / Maslina Abdul Aziz, Nurul Ain Mustakim and Shuzlina Abdul Rahman by Abdul Aziz, Maslina, Mustakim, Nurul Ain, Abdul Rahman, Shuzlina

“…The result indicated that the highest accuracy of 89.34% was achieved by the Random Tree algorithm, while the rule-based algorithm PART reached an accuracy of 87.56%. …”
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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data by Oyebayo, Olaniran Ridwan

“…Random Forests (RF) are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. …”
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Random forest algorithm for co2 water alternating gas incremental recovery factor prediction by Belazreg, L., Mahmood, S.M., Aulia, A.

“…RF develops multiple decision trees based on the random selection of the input data and random selection of the variables. …”
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Towards personalized intensive care decision support using a Bayesian network: A multicenter glycemic control study by Abu-Samah A., Razak N.N.A., Suhaimi F.M., Jamaludin U.K., Chase J.G.

“…Benchmarking; Decision support systems; Hospital data processing; Intensive care units; Patient treatment; Trees (mathematics); Blood glucose measurements; Classification precision; Discretization algorithms; Discretizations; Glycemic control; Performance prediction; Structure-learning; Variable selection; Bayesian networks…”

Gene Selection For Cancer Classification Based On Xgboost Classifier by Teo, Voon Chuan

“…XGBoost Classifier is applied in this research, which it is an efficient open-source implementation of the gradient boosted trees algorithm. Gradient boosting is a supervised learning algorithm, which attempts to accurately predict a target variable by combining the estimates of a set of simplifier, weaker models. …”
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Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study by Abu-Samah A., Abdul Razak N.N., Mohamad Suhaimi F., Jamaludin U.K., Chase G.

“…Decision support systems; Forecasting; Intensive care units; Medical informatics; Trees (mathematics); Accurate prediction; Causal Bayesian network; Discretization algorithms; Discretizations; Glycemic control; Intelligent mechanisms; Performance prediction; Variable selection; Bayesian networks…”

Tree species and aboveground biomass estimation using machine learning, hyperspectral and LiDAR data / Nik Ahmad Faris Nik Effendi by Nik Effendi, Nik Ahmad Faris

“…While the best predicted selection using RF is model 4 with mtry = p produced R2= 0.997 and RMSE=30.653kg/tree. …”
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Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit by Shah N.N.H., Razak N.N.A., Razak A.A., Abu-Samah A., Suhaimi F.M., Jamaluddin U.

“…Several machine learning algorithms which are decision tree, linear discriminant, na�ve Bayes, support vector machines, k-nearest neighbor, AdaBoost, and random forest were used for the classification. …”

Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance by Alsariera Y.A., Baashar Y., Alkawsi G., Mustafa A., Alkahtani A.A., Ali N.

Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques by Tofiq Y.M., Latif S.D., Ahmed A.N., Kumar P., El-Shafie A.

Coronary artery stenosis detection and visualization / Tang Sze Ling by Tang, Sze Ling

“…The performance evaluation results show that the stenosis detection algorithm performs better average sensitivity than several state-of-the-art algorithms.…”
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Automated model selection for corporation credit risk assessment using machine learning / Zulkifli Halim by Halim, Zulkifli

“…Machine learning model selection is an iterative process of exploring, evaluating, and improving algorithms. …”
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Machine learning classifications of multiple organ failures in a malaysian intensive care unit by Norliyana, Nor Hisham Shah, Normy Norfiza, Abdul Razak, Athirah, Abdul Razak, Asma’, Abu-Samah, Fatanah, M. Suhaimi, Ummu Kulthum, Jamaludin

“…Several machine learning algorithms which are decision tree, linear discriminant, naïve Bayes, support vector machines, k-nearest neighbor, AdaBoost, and random forest were used for the classification. …”
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