Boosting and margin theory

Feng, Jufu; Wang, Liwei; Sugiyama, Masashi; Yang, Cheng; Zhou, Zhi‐Hua; Zhang, Chicheng

doi:10.1007/s11460-012-0188-9

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…But due to the increasing of iterations, the efficiency of algorithm is greatly reduced. Because the adjustment for weights of samples is too small, researchers have proposed different methods to solve this problem [15,16] . In this paper a new weight adjusting method is proposed.…”

Section: Adaboostm1 Algorithm and Its Improvementmentioning

confidence: 99%

Intelligent Train Operation Models via KNN and Adaboost.M1

Zeng¹,

Chen²,

Li³

2013

IJACT

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Traditional control algorithms in Automatic Train Operation (ATO) system have some drawbacks, such as high energy consumption and low comfort. In this paper, two Intelligent Train Operation (ITO) models based on data mining for the subway train control are proposed combining with the driving experience. Firstly the training data set is sorted out and sieved out from the real train operation data set by drivers in Yizhuang-line of Beijing subway to establish the standard database. By using KNN and Adaboost.M1 respectively, two ITO models are dug out to represent the output of controller with speed limit, running time and gradient. In the train control simulation platform, ITO model is compared with the traditional PID control algorithm of ATO. The results show that the proposed ITO models perform better than PID control algorithm on saving energy, increasing comfortable degree, and reducing number of controller's output change. Moreover, Adaboost.M1 is better than KNN especially on energy consumption and comfort.

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Section: Adaboostm1 Algorithm and Its Improvementmentioning

confidence: 99%

Intelligent Train Operation Models via KNN and Adaboost.M1

Zeng¹,

Chen²,

Li³

2013

IJACT

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show abstract

“…Since local accuracy is a key feature of the DES method, many algorithms use k-nearest neighbors as a framework [19, 26, 27]. Other methods for generating different homogeneous classifiers have been proposed, including random subspace [28], bagging [29], boosting [30], and clustering [31].…”

Section: Introductionmentioning

confidence: 99%

A New Classification Method Based on Dynamic Ensemble Selection and its Application to Predict Variance Patterns in HIV-1 Env

Fili¹,

Han

Kort

et al. 2022

Preprint

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Therapeutics that target the envelope glycoproteins (Envs) of human immunodeficiency virus type 1 (HIV-1) effectively reduce virus levels in patients. However, due to mutations, new Env variants are frequently generated, which may be resistant to the treatments. The appearance of such sequence variance at any Env position is seemingly random. A better understanding of the spatiotemporal patterns of variance across Env may lead to the development of new therapeutic strategies. We hypothesized that, at any time point in a patient, positions with sequence variance are clustered on the three-dimensional structure of Env. To test this hypothesis, we examined whether variance at any Env position can be predicted by the variance measured at adjacent positions. Sequences from 300 HIV-infected patients were applied to a new algorithm we developed. The k-best classifiers (KBC) method is a dynamic ensemble selection technique that identifies the best classifier(s) within the neighborhood of a new observation. It applies bootstrap resampling to generate out-of-bag samples that are used with the resampled set to evaluate each classifier. For many positions of Env, primarily in the CD4-binding site, KBC accurately predicted variance based on the variance at their adjacent positions. KBC improved performance compared to the initial learners, static ensemble, and other baseline models. KBC also outperformed other algorithms for predicting variance at multi-position footprints of therapeutics on Env. These understandings can be applied to refine models that predict future changes in HIV-1 Env. More generally, we propose KBC as a new high-performance dynamic ensemble selection technique.

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Dynamic ensemble selection based on hesitant fuzzy multiple criteria decision making

Elmi

Eftekhari

2020

Soft Comput

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Boosting and margin theory

Cited by 7 publications

References 9 publications

Intelligent Train Operation Models via KNN and Adaboost.M1

Intelligent Train Operation Models via KNN and Adaboost.M1

A New Classification Method Based on Dynamic Ensemble Selection and its Application to Predict Variance Patterns in HIV-1 Env

Dynamic ensemble selection based on hesitant fuzzy multiple criteria decision making

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