Ensemble-based classifiers. Artificial Intelligence Review, 33(1-2):1-39, 2010.ROKACH, L. Ensemble-based classifiers. Artif. Intell. Rev., Kluwer Academic Publishers, Norwell, MA, USA, v. 33, n. 1-2, p. 1-39, fev. 2010. ISSN 0269-2821.L. Rokach, Ensemble-based classifiers, Artif...
Hu X (2001) Using rough sets theory and database operations to construct a good ensemble of classifiers for data mining applications. ICDM01. pp 233–240 Islam MM, Yao X, Murase K (2003) A constructive algorithm for training cooperative neural network ensembles. IEEE Trans Neural Netw 14(4...
Ensemble-based classifiers作者:Lior Rokach 摘要 The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. Researchers from various disciplines such as statistics and AI consider...
123 Ensemble-based classi?ers 11 Unlabeled Tuples Classifiers Composer Predicted Labels Classifier 1 Classifier 2 Classifier T Inducer 1 Inducer 2 ... Inducer T Dataset 1 Dataset 2 Dataset T Dataset Manipulator Dataset Manipulator Dataset Manipulator Training Set Fig. 6 Independent methods Fig. 7 ...
Ensemble-based Classifiers for Cancer Classification Using Human Tumor Microarray Data In this paper, two cancer classification techniques based on multicategory microarray data sets are presented. Due to the high dimensionality of microarray... A Margoosian,J Abouei - Electrical Engineering 被引量: ...
通过Random sphere cover classifiers (RSC)、random subspace 、boootstrap sampling 等方式生成新数据。 1.1.2 算法组合 对多种算法(参数、异构等)进行组合。基于基学习器的表现设置不同权重。基于类标签将训练观察的元数据平均,并利用测试样本上的预测与决策模板之间的距离来选择最合适的类标签。 1.2 改善集成表现 ...
Several studies have demonstrated the superior performance of ensemble classification algorithms, whereby multiple member classifiers are combined into one aggregated and powerful classification model, over single models. In this paper, two rotation-based ensemble classifiers are proposed as modeling techniques...
Ensemble-based detection techniques were also proposed for fault detection. Biao and Zhizhong in [64] developed an outlier detection method based on dynamic ensemble learning. Their proposed approach utilizes One-Class classifiers as base learners in the ensemble. To aggregate the results of the base...
Missing data and ensemble of classifiers Digital footprints and their usage for anomaly detection Elastic stack: background and scalability issues The main algorithm for classifying user profiles and detecting anomalies Experimental results Conclusions and future work Availability of data and materials Change...
The Chi-Square Statistical method was used for feature selection, and various ensemble classifiers, such as eXtreme gradient boosting (XGBoost), Bagging, extra trees (ET), random forest (RF), and AdaBoost can be used for the detection of intrusion applied to the Telemetry data of the TON_...