A Multi-level Validation Methodology for Wireless Network Applications

Drosos, Christos; Bisdounis, L.; Metafas, D. E.; Blionas, S.; Tatsaki, A.

doi:10.1007/978-3-540-30205-6_35

Cited by 3 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…this procedure was repeated 100 times for selecting a final marker gene set [21]. The consistency of the proposed approach was evaluated with a multi-level reproducibility validation framework [22]. It is a kind of level-by-level validation method [23].…”

Section: Multiple Rfe Selection Methods (12)mentioning

confidence: 99%

A Post-Method Condition Analysis of Using Ensemble Machine Learning for Cancer Prognosis and Diagnosis: a systematic review

Kavousi¹,

Mirsadeghi

Hosseini

et al. 2019

Preprint

View full text Add to dashboard Cite

Background Ensemble methods are supervised learning approaches that integrate different types of data or multiple individual classifiers. It has been shown that these methods can improve professional performance. Methods This study is an attempt to provide an in-depth review on 45 most relevant articles and aims to introduce 42 ensemble classifier (EC) machine learning methods used for the detection of 18 different types of cancer. Compared to other types of cancer, breast cancer, and the 22 ensemble methods introduced for its identification, is extensively investigated. The purpose of this study was to identify, map, and analyze the current academic discourse on EC machine learning methods in order to: 1. identify overarching themes emerging from empirical studies regarding EC methods, 2. determine their input data and decision-making strategies, and 3. evaluate relevant statistical procedures. Results By comparing various approaches, we can introduce Relevance Vector Machine (RVM)-based ensemble learning method that can provide optimal solutions for problems such as curse the dimensionality and high-dimensionality of feature space without missing data values. Conclusions To obtain robust performance and achieve better results, it is tactfully suggested to use multi-omics data integration, which has demonstrated to identify cancers and their subtypes more efficiently.

show abstract

Section: Multiple Rfe Selection Methods (12)mentioning

confidence: 99%