Results 51 to 60 of about 452,858 (310)
Ransomware Detection using Random Forest Technique
ICT Express, 2020 Nowadays, the ransomware became a serious threat challenge the computing world that requires an immediate consideration to avoid financial and moral blackmail. So, there is a real need for a new method that can detect and stop this type of attack.
Ban Mohammed Khammas
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Conditional Variable Importance for Random Forests [PDF]
, 2008 Random forests are becoming increasingly popular in many scientific fields because they can cope with ``small n large p'' problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been
Augustin Thomas +14 more
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Journal of Spectroscopy, 2018 Hyperspectral datasets contain spectral noise, the presence of which adversely affects the classifier performance to generalize accurately. Despite machine learning algorithms being regarded as robust classifiers that generalize well under unfavourable ...
Na’eem Hoosen Agjee +3 more
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International Journal of Approximate Reasoning, 2010 AbstractWhen individual classifiers are combined appropriately, a statistically significant increase in classification accuracy is usually obtained. Multiple classifier systems are the result of combining several individual classifiers. Following Breiman’s methodology, in this paper a multiple classifier system based on a “forest” of fuzzy decision ...
Piero P. Bonissone +3 more
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BarekengRandom forest algorithm allows for building better CART models. However, the disadvantage of this method is often underfitting, especially for small node sizes. Therefore, the double random forest method was developed to overcome this problem.
Arie Purwanto +2 more
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Random Forest Algorithm Based on Data Integration [PDF]
Jisuanji gongcheng, 2020 The historical data used for sales forecasting has the characteristics of sparseness and volatility,the traditional statistical or machine learning prediction algorithms for prediction perform poorly when the prediction cycle is long.Therefore,based on ...
XIE Kun, RONG Yutian, HU Fengping, CHEN Huan, YAO Xiaolong
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Pilar Nusa Mandiri, 2022 This study predicts and classifies benign and malignant breast cancer using 3 classification models. The method used in this research is Random Forest, Naïve Bayes and AdaBoost.
Bahtiar Imran +5 more
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Random forest machine learning technique for automatic vegetation detection and modelling in LiDAR data [PDF]
, 2021 Machine learning techniques have gained a distinguished position in the automatic processing of Light Detection and Ranging (LiDAR) data area. They represent the actual research topic in the remote sensing domain.
Tarsha Kurdi, Fayez +2 more
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SMOTE and Weighted Random Forest for Classification of Areas Based on Health Problems in Java
Journal of Applied Informatics and ComputingRandom Forest (RF) is a popular Machine Learning (ML) approach extensively employed for addressing classification issues. Nevertheless, the RF method for classification problems demonstrates suboptimal performance in cases of data imbalance.
Erwan Setiawan +2 more
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A Multi-Task Framework for Action Prediction
Information, 2020 Predicting the categories of actions in partially observed videos is a challenging task in the computer vision field. The temporal progress of an ongoing action is of great importance for action prediction, since actions can present different ...
Tianyu Yu +3 more
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