Search results for "Boosting"
showing 10 items of 59 documents
Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs.
2021
Background: Palliative care (PC) has demonstrated benefits for life-limiting illnesses. Cancer patients have mainly accessed these services, but there is growing consensus about the importance of promoting access for patients with non-malignant disease. Bad survival prognosis and patient9s frailty are usual dimensions to decide PC inclusion. Objectives: The main aim of this work is to design and evaluate three quantitative models based on machine learning approaches to predict frailty and mortality on older patients in the context of supporting palliative care decision making: one-year mortality, survival regression and one-year frailty classification. Methods: The dataset used in this stud…
A comparison of ensemble algorithms for item-weighted Label Ranking
2023
Label Ranking (LR) is a non-standard supervised classification method with the aim of ranking a finite collection of labels according to a set of predictor variables. Traditional LR models assume indifference among alternatives. However, misassigning the ranking position of a highly relevant label is frequently regarded as more severe than failing to predict a trivial label. Moreover, switching two similar alternatives should be considered less severe than switching two different ones. Therefore, efficient LR classifiers should be able to take into account the similarities and individual weights of the items to be ranked. The contribution of this paper is to formulate and compare flexible i…
Las funciones interactivas del marcador español ‘¿no?’ Las fronteras entre la atenuación y la protección de la imagen
2020
In this paper the functions of the Spanish discourse marker ?no? are analysed from a pragmatic and an interactive perspective. Specifically, we explore the values of ?no? taking the pragmatic phenomena of mitigation and boosting, as well as the notion of affiliation as described in conversation analysis. The previous literature devoted to the study of this linguistic form has consistently identified its uses as a confirmation request or a phatic device (Fuentes, 1990, 2009; Santos Rio, 2003; Garcia Vizcaino, 2005; Montanez, 2008, 2015; Rodriguez Munoz, 2009; Moccero, 2010; Santana, 2017). This work, however, analyses how the mitigating uses interact and share features with neighbouring cate…
The Engineering of a Compression Boosting Library: Theory vs Practice in BWT Compression
2006
Data Compression is one of the most challenging arenas both for algorithm design and engineering. This is particularly true for Burrows and Wheeler Compression a technique that is important in itself and for the design of compressed indexes. There has been considerable debate on how to design and engineer compression algorithms based on the BWT paradigm. In particular, Move-to-Front Encoding is generally believed to be an "inefficient " part of the Burrows-Wheeler compression process. However, only recently two theoretically superior alternatives to Move-to-Front have been proposed, namely Compression Boosting and Wavelet Trees. The main contribution of this paper is to provide the first ex…
From First Principles to the Burrows and Wheeler Transform and Beyond, via Combinatorial Optimization
2007
AbstractWe introduce a combinatorial optimization framework that naturally induces a class of optimal word permutations with respect to a suitably defined cost function taking into account various measures of relatedness between words. The Burrows and Wheeler transform (bwt) (cf. [M. Burrows, D. Wheeler, A block sorting lossless data compression algorithm, Technical Report 124, Digital Equipment Corporation, 1994]), and its analog for labelled trees (cf. [P. Ferragina, F. Luccio, G. Manzini, S. Muthukrishnan, Structuring labeled trees for optimal succinctness, and beyond, in: Proc. of the 45th Annual IEEE Symposium on Foundations of Computer Science, 2005, pp. 198–207]), are special cases i…
Decision Committee Learning with Dynamic Integration of Classifiers
2000
Decision committee learning has demonstrated spectacular success in reducing classification error from learned classifiers. These techniques develop a classifier in the form of a committee of subsidiary classifiers. The combination of outputs is usually performed by majority vote. Voting, however, has a shortcoming. It is unable to take into account local expertise. When a new instance is difficult to classify, then the average classifier will give a wrong prediction, and the majority vote will more probably result in a wrong prediction. Instead of voting, dynamic integration of classifiers can be used, which is based on the assumption that each committee member is best inside certain subar…
Dynamic Integration of Decision Committees
2000
Decision committee learning has demonstrated outstanding success in reducing classification error with an ensemble of classifiers. In a way a decision committee is a classifier formed upon an ensemble of subsidiary classifiers. Voting, which is commonly used to produce the final decision of committees has, however, a shortcoming. It is unable to take into account local expertise. When a new instance is difficult to classify, then it easily happens that only the minority of the classifiers will succeed, and the majority voting will quite probably result in a wrong classification. We suggest that dynamic integration of classifiers is used instead of majority voting in decision committees. Our…
Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures
2014
Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-…
Boosting the supercapacitive behavior of CoAl-layered double hydroxides via tuning the metal composition and interlayer space
2020
Layered double hydroxides (LDHs) are promising supercapacitor materials due to their wide chemical versatility, earth abundant metals and high specific capacitances. Many parameters influencing the supercapacitive performance have been studied such as the chemical composition, the synthetic approaches, and the interlayer anion. However, no systematic studies about the effect of the basal space have been carried out. Here, two-dimensional (2D) CoAl-LDHs were synthesized through anion exchange reactions using surfactant molecules in order to increase the interlayer space (ranging from 7.5 to 32.0 Å). These compounds exhibit similar size and dimensions but different basal space to explore excl…
Boosting the Performance of One-Step Solution-Processed Perovskite Solar Cells Using a Natural Monoterpene Alcohol as a Green Solvent Additive
2021
The perovskite film is the core of a perovskite solar cell (PSC), and its quality is crucial for the performance of such devices. The morphology, crystallinity, and surface coverage of the perovskite layer greatly affect the power conversion efficiency (PCE), hysteresis, and long-term stability of PSCs. The incorporation of appropriate solvent additives in the perovskite precursor solution is an effective strategy to control the film morphology and reduce the defects and grain boundaries. However, the commonly used solvent additives are environmentally harmful and highly toxic. In this work, α-terpineol (a nontoxic, eco-friendly, and low-cost monoterpene alcohol) is employed for the first t…