Pore engineering in double-wall MOFs through immobilizing functional bonding sites for boosting efficient ethane/ethylene separation
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Boosting
Boosting has been shown to improve the performance of classifiers in many situations, including when data is imbalanced. There are, however, two possible implementations of boosting, and it is unclear which should be used. Boosting by reweighting is typically used, but can only be applied to base learners which are designed to handle example weights. On the other hand, boosting by resampling can be applied to any base learner. In this work, we empirically evaluate the differences between these two boosting implementations using imbalanced training data. Using 10 boosting algorithms, 4 learners and 15 datasets, we find that boosting by resampling performs as well as, or significantly better than, boosting by reweighting (which is often the default boosting implementation). We therefore conclude that in general, boosting by resampling is preferred over boosting by weighting.
Boosting
Resampling
Gradient boosting
Implementation
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Boosting
Gradient boosting
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Changes in the data distribution (concept drift) makes online learning a challenge that is progressively attracting more attention. This paper proposes Boosting-like Online Learning Ensemble (BOLE) based on heuristic modifications to Adaptable Diversity-based Online Boosting (ADOB), which is a modified version of Oza and Russell's Online Boosting. More precisely, we empirically investigate the effects of (a) weakening the requirements to allow the experts to vote and (b) changing the concept drift detection method internally used, aiming to improve the ensemble accuracy. BOLE was tested against the original and other modified versions of both boosting methods as well as three renowned ensembles using well-known artificial and real-world datasets and statistically surpassed the accuracies of both boosting methods as well as those of the three ensembles. The accuracy improved in most tested situations but this is more evident in the datasets with more concept drifts, where the accuracy gains were very high.
Boosting
Ensemble Learning
Gradient boosting
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Boosting
Gradient boosting
Ensemble Learning
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Boosting
Gradient boosting
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Boosting is a general method for improving the accuracy of any given learning algorithm. This short paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting. Some examples of recent applications of boosting are also described.
Boosting
Overfitting
AdaBoost
Gradient boosting
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Boosting
AdaBoost
Gradient boosting
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As shown in the bibliography, training an ensemble of networks is an interesting way to improve the performance with respect to a single network. The two key factors to design an ensemble are how to train the individual networks and how to combine them to give a single output. Boosting is a well known methodology to build an ensemble. Some boosting methods use an specific combiner (Boosting Combiner) based on the accuracy of the network. Although the Boosting combiner provides good results on boosting ensembles, the simple combiner Output Average worked better in three new boosting methods we successfully proposed in previouses papers. In this paper, we study the performance of sixteen different combination methods for ensembles previously trained with Adaptive Boosting and Average Boosting in order to see which combiner fits better on these ensembles. Finally, the results show that the accuracy of the ensembles trained with these original boosting methods can be improved by using the appropriate alternative combiner. In fact, the Output average and the Weighted average on low/medium sized ensembles provide the best results in most of the cases.
Boosting
Gradient boosting
Ensemble Learning
AdaBoost
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Бұл зерттеужұмысындaКaно моделітурaлы жәнеоғaн қaтыстытолықмәліметберілгенжәнеуниверситетстуденттерінебaғыттaлғaн қолдaнбaлы (кейстік)зерттеужүргізілген.АхметЯссaуи университетініңстуденттеріүшін Кaно моделіқолдaнылғaн, олaрдың жоғaры білімберусaпaсынa қоятынмaңыздытaлaптaры, яғнисaпaлық қaжеттіліктері,олaрдың мaңыздылығытурaлы жәнесaпaлық қaжеттіліктерінеқaтыстыөз университетінқaлaй бaғaлaйтындығытурaлы сұрaқтaр қойылғaн. Осы зерттеудіңмaқсaты АхметЯсaуи университетіндетуризмменеджментіжәнеқaржы бaкaлaвриaт бaғдaрлaмaлaрыныңсaпaсынa қaтыстыстуденттердіңқaжеттіліктерінaнықтaу, студенттердіңқaнaғaттaну, қaнaғaттaнбaу дәрежелерінбелгілеу,білімберусaпaсын aнықтaу мен жетілдіружолдaрын тaлдaу болыптaбылaды. Осы мaқсaтқaжетуүшін, ең aлдыменКaно сaуaлнaмaсы түзіліп,116 студенткеқолдaнылдыжәнебілімберугежәнеоның сaпaсынa қaтыстыстуденттердіңтaлaптaры мен қaжеттіліктерітоптықжұмыстaрaрқылыaнықтaлды. Екіншіден,бұл aнықтaлғaн тaлaптaр мен қaжеттіліктерКaно бaғaлaу кестесіменжіктелді.Осылaйшa, сaпa тaлaптaры төрт сaнaтқa бөлінді:болуытиіс, бір өлшемді,тaртымдыжәнебейтaрaп.Соңындa,қaнaғaттaну мен қaнaғaттaнбaудың мәндеріесептелдіжәнестуденттердіңқaнaғaттaну мен қaнaғaттaнбaу деңгейлерінжоғaрылaту мен төмендетудеосытaлaптaр мен қaжеттіліктердіңрөліaйқын aнықтaлды.Түйінсөздер:сaпa, сaпaлық қaжеттіліктер,білімберусaпaсы, Кaно моделі.
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The nationally-recognized Susquehanna
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working at an extremely high
musical level.AÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂA¢AÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂA Experience choral
singing that will take you to new
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