Search Results - see tree algorithm

Seeing trees from space: above-ground biomass estimates of intact and degraded montane rainforests from high-resolution optical imagery by Phua, Mui How, Ling, Zia-Yiing, David Anthony Coomes, Wilson Wong, Alexius Korom, Satoshi Tsuyuki, Keiko Ioki, Yasumasa Hirata, Hideki Saito, Gen Takao

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Behaviour Analysis Among Adolescents And Children For Cyberbullying Based On Twitter And Kaggle Dataset by Afiefah Hannani, Abdul Halim

“…The purpose of this study is to see the accuracy of the algorithms and compare it. …”
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Investigation of Data Mining Using Pruned Artificial Neural Network Tree by Kalaiarasi, S. M. A., Sainarayanan, Gopala, Ali Chekima, Jason Teo

“…Two pruning techniques are used with the ANNT algorithm; one is to prune the neural network and another to prune the tree. …”
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Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids by Chiaverini, Luca, Macdonald, David W., Hearn, Andrew J., Kaszta, Zaneta, Ash, Eric, Bothwell, Helen M., Can, Ozgun Emre, Channa, Phan, Clements, Gopalasamy Reuben *, Haidir, Iding Achmad, Kyaw, Pyae Phyoe, Moore, Jonathan H., Rasphone, Akchousanh, Tan, Cedric Kai Wei, Cushman, Samuel A.

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Decision Tree: Customer churn analysis for a loyalty program using data mining algorithm by Lee, Angela Siew Hoong *, Zuraini Zainol, Ng, Claudia, Chan, Khin Whai

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Classification of basal stem rot disease in oil palm using dielectric spectroscopy by Al-Khaled, Al-Fadhl Yahya Khaled

“…An analysis was done to see the effect of implementing different data reduction algorithms in classifying BSR disease. …”
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Hypertension Prediction in Adolescents Using Anthropometric Measurements: Do Machine Learning Models Perform Equally Well? by Chai, Soo See, Goh, Kok Luong, Cheah, Whye Lian, Chang, Robin Yee Hui, Ng, Giap Weng

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Performance metrices analysis for diabetes prediction using machine learning algorithm by Said Omar, Saida, Mohamad Zulkufli, Nurul Liyana, Ahmad Puzi, Asmarani, Shah, Asadullah

“…While the second dataset was coming through Sylhet Diabetes Hospital in Bangladesh with the small number of instances. To see the behaviour of these dataset, each dataset used three machine learning algorithm which were Naïve Bayes, Random Forest and Decision tree (J48). …”
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Classification of cervical cancer using random forest by Bahirah, Mohd Bashah, Ku Muhammad Naim, Ku Khalif, Nor Azuana, Ramli

“…In this research, the cervical cancer risk classification model was used by using data mining approach which consider Decision Tree and Random Forest algorithm. These two models have been implemented by using JupyterLab on the UCI datasets. …”
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Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well? by Chai, Soo See, Goh, Kok Luong, Cheah, Whye Lian, Chang, Yee Hui Robin, Ng, Giap Weng

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The analysis of road traffic fatality pattern for Selangor, Malaysia case study by Radzuan, N. Q., Mohd Hasnun Ariff, Hassan, Abu Kassim, K. A., Ab. Rashid, A. A., Intan Suhana, Mohd Razelan, Nur Aqilah, Othman

“…The analysed algorithms among others are neural network, random forest, decision tree, logistic regression, naïve Bayes, and support vector machine. …”
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Application of machine learning in analysing historical and non-historical characteristics of heritage pre-war shophouses / Nur Shahirah Ja’afar and Junainah Mohamad by Ja’afar, Nur Shahirah, Mohamad, Junainah

“…Several Machine Learning algorithms have been developed, namely Random Forest Regressor, Decision Tree Regressor, Lasso Regressor, Ridge Regressor and Linear Regressor. …”
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Design and analysis of an early heart attack detection using openCV by Muhammad Rafsanjani, Basri, Fahmi, Samsuri

“…Hyper-parameter tuning method also has been applied to see does the algorithm increase accuracy or not.…”
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A new history matching sensitivity analysis framework with random forests and Plackett-Burman design by Aulia, A., Jeong, D., Mohd Saaid, I., Shuker, M.T., El-Khatib, N.A.

“…Hence, parameters with low impact on the history matching error are discarded, and the remaining are used for Genetic Algorithm-based automatic history matching. The impact of an internal Random Forests parameter (number of decision trees) on the history matching error is also observed. …”
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