Rokach, L. (2010). Ensemble-based classifiers. Artif. Intell. Rev. 33, 1-39. doi: 10.1007/s10462-009-9124-7L. Rokach, "Ensemble-based classifiers," Artif. Intell. Rev., vol. 33, 2010, pp. 1-39.Lior Rokach. Ensemble-based classifiers. Artificial Intelligence Review, 33(1-2):1-...
· Boosting1 IntroductionThe purpose of supervised learning is to classify patterns (also known as instances) into aset of categories which are also referred to as classes or labels. Commonly, the classificationis based on a classification models (classifiers) that are induced from an exemplary set...
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: ...
【具体算法】 该方法应用于异构集合系统中,在给定的训练集上通过训练K种不同的学习算法获得EoC(Ensemble of Classifiers),并通过组合算法对所有基学习器的预测进行聚合,获得最终的预测。基础学习器有:LDA、Naive Bayes和kNN(k =5)。训练阶段的伪代码如下: 然后,利用ABC算法来寻找较优的L_{0-1}值。 ABC算法简介...
Such classifiers are a combination of both approaches and their activities can be divided into two steps. Selection of key features. Input data is preprocessed by algorithms, which extract the key features from the input objects. For feature extraction, there is no general rule. Their choice is...
The obtained results demonstrated the advantage of ensemble classifiers over single-learning classification models. When ensemble learning was used instead of the single learning, the average F1 score, accuracy, and AUC of the models increased by 2.17%, 1.66%, and 6.27%, respectively. In particular...
After that, a new ensemble classifier is developed using the stacked ensemble method by combining the prediction effects of all those base classifiers, where a new model learns how to better integrate predictions from multiple base models. We used the two-stage stacking technique [52]. First, ...
of ELM to avoid the randomness andinstability of classification result when ELM uses randomly generated parameters, and majorityvoting strategy is used to fuse the classification results of multiple base classifiers to avoid thenegative impact of ELM with local optimal parameters on classification result...
38. Stacking involves combining the predictions of multiple models using a meta-classifier39. With the use of decision trees and a famous ensemble technique called random forest, intrusion detection accuracy may be increased40. While Random Forests are powerful classifiers, their decision-making ...
Any classification method, such as decision trees, lazy classifiers, neural networks, or other types of learners, may be used as the base learner. According to its diversity, an ensemble scheme may use a single classifier to create a homogenous ensemble. In contrast, others may employ a ...