Applying ant colony optimization to configuring stacking ensembles for data mining
, 2014 An ensemble is a collective decision-making system which applies a strategy to combine the predictions of learned classifiers to generate its prediction of new instances.
CHEN, Yi Jun +2 more
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Ensemble Prediction via Covariate-dependent Stacking
Statistics and Computing27 ...
Wakayama, Tomoya, Sugasawa, Shonosuke
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Stacked regression ensemble for cancer class prediction [PDF]
INDIN '05. 2005 3rd IEEE International Conference on Industrial Informatics, 2005., 2005 Design of a machine learning algorithm as a robust class predictor for various DNA microarray datasets is a challenging task, as the number of samples are very small as compared to the thousands of genes (feature set). For such datasets, a class prediction model could be very successful in classifying one type of dataset but may fail to perform in a ...
Sehgal, M. Shoaib +2 more
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Application of bagging, boosting and stacking to intrusion detection
, 2012 This paper investigates the possibility of using ensemble algorithms to improve the performance of network intrusion detection systems. We use an ensemble of three different methods, bagging, boosting and stacking, in order to improve the accuracy and ...
Prugel-Bennett, Adam +3 more
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A novel improved model for building energy consumption prediction based on model integration [PDF]
, 2020 Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems.
Feng, W, Lu, S, Wang, R
core
Stacking-fault energies for Ag, Cu, and Ni from empirical tight-binding potentials
, 2002 The intrinsic stacking-fault energies and free energies for Ag, Cu, and Ni are derived from molecular-dynamics simulations using the empirical tight-binding potentials of Cleri and Rosato [Phys. Rev. B 48, 22 (1993)].
A. Girshick +10 more
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Ensemble Machine Learning Approach for Anemia Classification Using Complete Blood Count Data
Al-Mustansiriyah Journal of ScienceBackground: Anemia is a widespread global health issue affecting millions of individuals worldwide. Early and accurate diagnosis is essential for effective treatment. Traditional diagnostic approaches rely on complete blood count (CBC) parameters, which
Rasha Jamal Hindi
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A greedy stacking algorithm for model ensembling and domain weighting
BMC Research Notes, 2020 Objective Because it is impossible to know which statistical learning algorithm performs best on a prediction task, it is common to use stacking methods to ensemble individual learners into a more powerful single learner.
Christoph F. Kurz +2 more
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Modeling brand choice using boosted and stacked neural networks [PDF]
The brand choice problem in marketing has recently been addressed with methods from computational intelligence such as neural networks. Another class of methods from computational intelligence, the so-called ensemble methods such as boosting and stacking
Potharst, R. +2 more
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Analytical description of finite size effects for RNA secondary structures
, 2005 The ensemble of RNA secondary structures of uniform sequences is studied analytically. We calculate the partition function for very long sequences and discuss how the cross-over length, beyond which asymptotic scaling laws apply, depends on thermodynamic
I. L. Hofacker +6 more
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