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Constructing decision functions with augmented ordinal information

R.R. Yager
Proceedings of the 33rd Annual Hawaii International Conference on System Sciences  
For example, at the most Our concern is with the problem of constructing powerful extreme is the absolute scale, here all decision functions to aid in making decision under mathematical operations are  ...  This allows us to have However, the price we pay for ease of burden is a loss information such as A is preferred to B but both of operations. With this ordinal scale we have only are acceptable.  ...  that A ≥ A, a k ≥ a k for k, then progressive decision functions lead's to simplicity in the construction of compound decision functions .  ... 
doi:10.1109/hicss.2000.926664 dblp:conf/hicss/Yager00 fatcat:nakauviuzbcz3jdncwla664kia

An ordinal CNN approach for the assessment of neurological damage in Parkinson's disease patients

Javier Barbero-Gómez, Pedro-Antonio Gutiérrez, Víctor-Manuel Vargas, Juan-Antonio Vallejo-Casas, César Hervás-Martínez
2021 Expert systems with applications  
Given that CNNs need large datasets to achieve acceptable performance, a data augmentation method is adapted to work with spatial data.  ...  We show how the ordinal methodology improves the performance with respect to the nominal one, and how OGO-SP-β yields better performance than OGO-SP.  ...  s with Markov Random Field models to augment 3D functional MRI! multi-subject data and enhance nominal classification performance.  ... 
doi:10.1016/j.eswa.2021.115271 fatcat:ziwh6rif4fdc7htejfm4nee75e

Giving the Expectancy-Value Model a Heart

Victor Henning, Thorsten Hennig-Thurau, Stephanie Feiereisen
2012 Psychology & Marketing  
This manuscript augments the classical expectancy-value model of attitude with a dimensional model of emotion.  ...  Over the past decade, research in consumer behavior has debated the role of emotion in consumer decision making intensively but has offered few attempts to integrate emotion-related findings with established  ...  With a controlled experiment involving 308 college students faced with actual purchase decisions, the authors test whether the augmented EVM performs better than the traditional EVM in predicting overall  ... 
doi:10.1002/mar.20562 fatcat:sgyr54vbq5ccjbnrzxekwz7bxm

Prediction trees with soft nodes for binary outcomes

Antonio Ciampi, Andr� Couturier, Shaolin Li
2002 Statistics in Medicine  
This means that at each node an individual goes to the right branch with a certain probability, function of a predictor.  ...  We propose a new algorithm for the construction of a tree-structured predictor for the event of interest, which uses a new approach for dealing with continuous predictors.  ...  As remarked by a referee, it is natural to also think of constructing decision functions based on all the ordinal variables, a very useful suggestion for further work.  ... 
doi:10.1002/sim.1106 pmid:11933039 fatcat:iioejyxipfacnjmdapvm4vic2u

A Co-designed Hardware/Software Architecture for Augmented Materials [chapter]

Simon Dobson, Kieran Delaney, Kafil Mahmood Razeeb, Sergey Tsvetkov
2005 Lecture Notes in Computer Science  
We present an architecture for the hardware/software co-design of such "augmented" materials that allows designers to address the links between the physical and informational properties of artefacts.  ...  A co-designed hardware/software architecture for augmented materials. In  ...  Augmenting materials with information technology addresses this gap, allowing materials to process information as well as functioning physically, reflecting on its behaviour and providing feedback to the  ... 
doi:10.1007/11569510_5 fatcat:fkrfztlh3vdelgibqnano2nvim

Kernel combination via debiased object correspondence analysis

David Windridge, Fei Yan
2016 Information Fusion  
where the correspondence information is given a priori.  ...  We benchmark the method against the augmented kernel method, an order-insensitive approach derived from the direct sum of constituent kernel matrices, and also against straightforward additive kernel combination  ...  The relevant information is obtainable from the final correspondence set S l (n) in conjunction with the original pattern vector ordinates.  ... 
doi:10.1016/j.inffus.2015.02.002 fatcat:jfzhoeuunzcmpm7qukn5wvg4ty

Ordinal Triplet Loss: Investigating Sleepiness Detection from Speech

Peter Wu, SaiKrishna Rallabandi, Alan W. Black, Eric Nyberg
2019 Interspeech 2019  
By nature, the given speech dataset is an archetype of one with relatively limited samples, a complex underlying data distribution, and subjective ordinal labels.  ...  We propose a novel approach termed ordinal triplet loss (OTL) that can be readily added to any deep architecture in order to address the above data constraints.  ...  Ordinal Triplet Loss Ordinal triplet loss augments the traditional triplet loss function [28] by capturing ordinal relations, thus further utilizing properties in a limited corpus.  ... 
doi:10.21437/interspeech.2019-2278 dblp:conf/interspeech/WuRBN19 fatcat:j4zsp2nbsjfgpnaijur4okvy5u

Transductive Ordinal Regression

Chun-Wei Seah, I. W. Tsang, Yew-Soon Ong
2012 IEEE Transactions on Neural Networks and Learning Systems  
The key challenge of the present study lies in the precise estimation of both the ordinal class label of the unlabeled data and the decision functions of the ordinal classes, simultaneously.  ...  Ordinal regression is commonly formulated as a multi-class problem with ordinal constraints.  ...  ordinal decision functions is introduced.  ... 
doi:10.1109/tnnls.2012.2198240 pmid:24807134 fatcat:wturc4ja5ndopp4w2rfqwhritm

Ordinal Regression Methods: Survey and Experimental Study

Pedro Antonio Gutierrez, Maria Perez-Ortiz, Javier Sanchez-Monedero, Francisco Fernandez-Navarro, Cesar Hervas-Martinez
2016 IEEE Transactions on Knowledge and Data Engineering  
This SVM model is combined with a proposed label swapping scheme for multiple class transduction to derive ordinal decision boundaries that pass through a low-density region of the augmented labelled and  ...  In [32] , ordinal meta-models were compared with respect to their nominal counterparts to check their ability to exploit ordinal information.  ... 
doi:10.1109/tkde.2015.2457911 fatcat:vm6ho4crurap3o6gbgb6pyypye

Utilizing Surrogate Numbers for Probability Elicitation [chapter]

Mats Danielson, Love Ekenberg, Andreas Paulsson
2018 Decision Making  
Furthermore, when decision-makers possess more information regarding the relative strengths of probabilities, that is, some form of cardinality, the input information to ordinal methods is sometimes too  ...  Many alternative methods to resolve this complication have been suggested over the years, including procedures for dealing with incomplete information.  ...  Acknowledgements This research was funded by the Swedish Research Council FORMAS, project number 2011-3313-20412-31, as well as by Strategic funds from the Swedish government within Information and Communications  ... 
doi:10.5772/intechopen.76422 fatcat:rbmikmliardwbkb3j2spxhz7ve

Agreement and Reliability Analysis of Machine Learning Scaling and Wireless Monitoring in the Assessment of Acute Proximal Weakness by Experts and Non-Experts: A Feasibility Study

Eunjeong Park, Kijeong Lee, Taehwa Han, Hyo Suk Nam
2022 Journal of Personalized Medicine  
MRC scaling requires an ordinal classification with skewed distribution in multiple classes.  ...  The Bayesian update modifies the Gaussian process model at each new evaluation of the objective function f(x), and the acquisition function a(x) based on the Gaussian process model of f(x) is maximized  ...  Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jpm12010020 pmid:35055335 pmcid:PMC8780198 fatcat:qybjtkdsvbflppqlh7etxmmn5e

The Robustness Concern in Preference Disaggregation Approaches for Decision Aiding: An Overview [chapter]

Michael Doumpos, Constantin Zopounidis
2014 Optimization in Science and Engineering  
The construction of a value function from a set of reference examples can be performed with mathematical programming formulations.  ...  Thus, they augmented the objective function of problems (6)-(7) considering not only the error variables, but also the complexity of the resulting value function.  ... 
doi:10.1007/978-1-4939-0808-0_8 fatcat:gkk5mip3lfdofph33ju34t77ae

Binary and Ordinal Data Analysis in Economics: Modeling and Estimation [chapter]

Ivan Jeliazkov, Mohammad Arshad Rahman
2013 Mathematical Modeling with Multidisciplinary Applications  
The basic setup involves utility maximizing decision makers, who choose among competing alternatives associated with certain levels of utility.  ...  Turning attention to ordinal outcomes, equation (1.5) and the assumption of independent sampling give the following likelihood function for the ordinal data model f (y|β, γ) = n ∏ i=1 Pr(y i |β, γ) = n  ... 
doi:10.1002/9781118462706.ch6 fatcat:nxhuanqu3jdbpns7hbnw4nv3da

Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation

Peipeng Wang, Xiuguo Zhang, Zhiying Cao
2021 Applied Sciences  
Therefore, we propose a model with data augmentation and feature augmentation for few-shot charge prediction.  ...  Then, the charge information heterogeneous graph is introduced, and a novel graph convolutional network is designed to extract distinguishability features for feature augmentation.  ...  Therefore, we propose a novel model with data augmentation and feature augmentation for few-shot charge prediction.  ... 
doi:10.3390/app112210811 fatcat:j7h3udh2zjfy3ojhpchh7t2bbi

A Two-Sided Matching Decision Model Based on Uncertain Preference Sequences

Xiao Liu, Huimin Ma
2015 Mathematical Problems in Engineering  
We also compare our decision model with two other approaches, and summarize their characteristics on two-sided matching.  ...  matching effect, and then solve it with branch-and-bound algorithm.  ...  No matter whether it is a linear function or a nonlinear function, both depend on the specific decision background.  ... 
doi:10.1155/2015/241379 fatcat:mfrjeaqwcbefzntcb4q3v6uspe
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