Bagging Classification Models with Reduced Bootstrap

Pino‐Mejías, Rafael; Cubiles-de-la-Vega, María-Dolores; López-Coello, Manuel; Silva-Ramírez, Esther-Lydia; Jiménez-Gamero, M. D.

doi:10.1007/978-3-540-27868-9_106

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Alternative bootstrap resampling scheme is also presented in Jimnez-Gamero et al (2004), which only uses a portion of the set of all possible bootstrap samples. Rafael Pino-Mejias et al (2004) proposed another alternative bootstrap procedure, namely the reduced bootstrap, which only uses a portion of the set of all possible bootstrap samples, as the sampling algorithm for bagging.…”

Section: Alternative Sampling Bagging Variantsmentioning

confidence: 99%

Bagging and boosting variants for handling classifications problems: a survey

Kotsiantis

2013

The Knowledge Engineering Review

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Bagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. Since bagging and boosting are an effective and open framework, several researchers have proposed their variants, some of which have turned out to have lower classification error than the original versions. This paper tried to summarize these variants and categorize them into groups. We hope that the references cited cover the major theoretical issues, and provide access to the main branches of the literature dealing with such methods, guiding the researcher in interesting research directions.

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Section: Alternative Sampling Bagging Variantsmentioning

confidence: 99%

Bagging and boosting variants for handling classifications problems: a survey

Kotsiantis

2013

The Knowledge Engineering Review

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Holistic learning and optimization scheme for wireless networks

Husen,

Chaudary,

Ahmad

et al. 2024

Trans Emerging Tel Tech

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Future networks are expected to use Artificial Intelligence and machine learning applications intensely. The primary focus of machine learning techniques will be on intelligent automation and decision‐making based on information learned from network functions, resources, and users. The research community has studied several schemes for using machine learning in communications and networks in the last two decades. However, the schemes mainly focus on learning the state information of individual network functions and resources and performing their optimization in a standalone fashion. Recently, a study group from the International Telecommunication Union has outlined machine learning pipeline requirements. It addresses several aspects of machine learning operations, such as model selection, data sources, and action points. However, the mechanisms to define the state of network functions and resources for different network layers and domains are yet to be explored. Therefore, in addition to the above, specific studies are required for cross‐layer and administrative domain knowledge sharing and holistic optimization. Without considering the holistic view, applying machine learning to optimizing individual functions and resources may result in nonoptimal and unexpected behaviors instead of having any positive effect. The need for global and deep holistic learning has recently been identified in the literature; however, no implementation of the above scheme has been studied or evaluated for networks. This article proposes a novel approach, implementing global and deep holistic learning in wireless networks toward fulfilling the requirements of future intelligent networks. It also proposes an objective function‐based feature engineering for wireless networks. Experimental results of the proposed scheme show significant improvements in the network performance and accuracy of the machine learning models. Moreover, applications of transfer learning to base learners for network functions and resources can significantly expedite decision‐making with reduced computational costs.

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Applying Bagging Techniques to the SA Tabu Miner Rule Induction Algorithm

Chorbev¹,

Andovska

2010

ICT Innovations 2009

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Bagging Classification Models with Reduced Bootstrap

Cited by 4 publications

References 12 publications

Bagging and boosting variants for handling classifications problems: a survey

Bagging and boosting variants for handling classifications problems: a survey

Holistic learning and optimization scheme for wireless networks

Applying Bagging Techniques to the SA Tabu Miner Rule Induction Algorithm

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