Ensemble Methods
Hierarchical ensemble methods for protein function prediction
... top-down ensemble methods significantly outperform decision trees-based hierarchical methods, but no significant difference between different flavors of top- down hierarchical ensembles has been ...
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Ensemble Methods to Distinguish Mainland and Taiwan Chinese
... This paper describes the IUCL system at Var- Dial 2019 evaluation campaign for the task of discriminating between Mainland and Taiwan variation of mandarin Chinese. We first build several base classifiers, including a ...
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Efficient Ensemble Methods for Document Clustering
... analysis methods have often involved the repeated execution of a clustering procedure, followed by the manual selection of an individual solution that maximises a user-defined ...an ensemble of clusterings ...
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Evaluation of Ensemble methods for uplift modeling
... 2008). Ensemble methods are a class of highly successful machine learning algorithms which combine several different models to obtain an ensemble which is, hopefully, more accurate than its ...
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Combination of Several Control Charts Based on Dynamic Ensemble Methods
... Combining methods from Statistical Process Control (SPC) in order to benefit from more than one method’s efficiency has been recently ...profit. Ensemble methods from data mining domain have recently ...
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Ensemble methods in multi-label classification
... learning methods as proposed in the ...multi-label ensemble method, named LDsplit with ...learning methods, by performing an empirical study on benchmark ...multi-label ensemble ...
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Ensemble methods in ordinal data classification
... One way to improve the results of supervised learning tasks is by combining various models via ensemble methods, which have been shown to usually have better results than any single classifier [Die00]. ...
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Ensemble methods for instance-based Arabic language authorship attribution
... Authorship Attribution (AA) problem in Arabic language has been addressed in quite few studies and several analysis methods were applied to tackle the issue. However , the performance of these methods needs ...
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Effect of Ensemble Methods for Software Fault Prediction at Various Metrics Level
... classifiers ensemble can effectively improve classification performance than a single ...various ensemble methods with perspective of taxonomy. The ensemble methods include Bagging, ...
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Ensemble methods in intrusion detection
... The methods employed in intrusion detection could vary from single, to hybrid to ...Single methods refers to the use of a classifier or a technique used in the detection engine of either a host based, ...
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Survey on Lazy Ensemble Methods for Improving Accuracy of Lazy Learner
... and ensemble approach for build lazy learner. Lazy ensemble is build using ensemble approach that uses multiple classifiers and combines its predictions using combination methods and find ...
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Efficient Intrusion Detection Model Using Ensemble Methods
... of ensemble learners: homogeneous learners and heterogeneous ...learners. Ensemble methods use a single base learning algorithm produces homogeneous learners, ...an ensemble is often much ...
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Improving Statistical Word Alignment with Ensemble Methods
... use ensemble methods [5], [6]. The ensemble methods were proposed to improve the performance of ...An ensemble of classifiers is a set of classifiers whose individual deci- sions are ...
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Characterization of neurodegenerative diseases with tree ensemble methods: the case of Alzheimer's disease
... ML methods in neuroimaging is Support Vector Ma- chines (SVM) [Hearst et ...these methods typically use the whole set of voxels to compute a prediction and, so, it is difficult to threshold the weights and ...
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New Ensemble Methods For Evolving Data Streams
... classifier methods: they are easy to scale and parallelize, they can adapt to change quickly by pruning under-performing parts of the ensemble, and they therefore usually also generate more accurate concept ...
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Ensemble Methods for Automatic Thesaurus Extraction
... It turns out that the average Spearman rank-order correlation is not sensitive enough to errors for the purposes of comparing favourable disagreement within ensembles. However, the average comple- mentarity clearly shows ...
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Randomness In Tree Ensemble Methods
... tree ensemble techniques ...tree ensemble acts by forming a locally adaptive distance metric [Lin and Jeon, ...ensembles methods and argue that this insight can help to explain the exceptional ...
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Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods
... The ensemble and hybrid models are the new generations of machine ...learning methods. Bagging and boosting methods are reported as the most popular technique to build ...computing methods to ...
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Ensemble Methods for Anomaly Detection
... of ensemble methods for unsupervised outlier detection is an emerging research topic, addressed in only a few works so far, such as [6, 9, 39, 44, 74]; of these the work in [6] is the only attempt to apply ...
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Ensemble Methods for Native Language Identification
... Our team—Uvic-NLP—explored and evaluated a variety of lexical features for Native Language Identification (NLI) within the framework of ensemble methods. Using a subset of the highest- performing features, ...
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