Predicting Trait Impressions of Faces Using Classifier Ensembles

2009 ◽  
pp. 403-439 ◽  
Author(s):  
Sheryl Brahnam ◽  
Loris Nanni
Keyword(s):  
Classifier Ensembles ◽  
Trait Impressions

Expectations about pain and analgesic treatment depend on medical providers’ facial appearances

2020 ◽  
Author(s):  
Elizabeth A. Necka ◽  
Carolyn Amir ◽  
Troy C. Dildine ◽  
Lauren Yvette Atlas
Keyword(s):  
Patient Outcomes ◽  
Visual Information ◽  
Healthcare Providers ◽  
Medical Procedures ◽  
Medical Providers ◽  
Analgesic Treatment ◽  
Digital Services ◽  
Receive Treatment ◽  
Facial Images ◽  
Trait Impressions

There is a robust link between patients’ expectations and clinical outcomes, as evidenced by the placebo effect. These expectations are shaped by the context surrounding treatment, including the patient-provider interaction. Prior work indicates that the provider’s behavior and characteristics, including warmth and competence, can shape patient outcomes. Yet humans rapidly form trait impressions of others prior to any in-person interaction. Here, we tested whether trait-impressions of hypothetical medical providers, based purely on facial images, influence participants’ choice of medical providers and expectations about their health following hypothetical medical procedures performed by those providers in a series of vignettes. Across five studies, participants selected providers who appeared more competent, based on facial visual information alone. Further, providers’ apparent competence predicted participants’ expectations about post-procedural pain and medication use. Participants’ perception of their similarity to providers also shaped expectations about pain and treatment outcomes. Our results suggest that humans develop expectations about their health outcomes prior to even setting foot in the clinic, based exclusively on first impressions. These findings have strong implications for health care, as individuals increasingly rely on digital services to choose healthcare providers, schedule appointments, and even receive treatment and care, a trend which is exacerbated as the world embraces telemedicine.

scholarly journals Impact of the learners diversity and combination method on the generation of heterogeneous classifier ensembles

2021 ◽  
pp. 107689
Author(s):  
M. Paz Sesmero ◽  
José Antonio Iglesias ◽  
Elena Magan ◽  
Agapito Ledezma ◽  
Araceli Sanchis
Keyword(s):  
Combination Method ◽  
Classifier Ensembles

scholarly journals Testing the Suitability of Automated Machine Learning for Weeds Identification

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 34-47
Author(s):  
Borja Espejo-Garcia ◽  
Ioannis Malounas ◽  
Eleanna Vali ◽  
Spyros Fountas
Keyword(s):  
Machine Learning ◽  
Plant Protection ◽  
Crop Protection ◽  
Identification Problem ◽  
Learning System ◽  
Classifier Ensembles ◽  
Automated Machine Learning ◽  
A New Technique ◽  
Plant Seedlings ◽  
And Training

In the past years, several machine-learning-based techniques have arisen for providing effective crop protection. For instance, deep neural networks have been used to identify different types of weeds under different real-world conditions. However, these techniques usually require extensive involvement of experts working iteratively in the development of the most suitable machine learning system. To support this task and save resources, a new technique called Automated Machine Learning has started being studied. In this work, a complete open-source Automated Machine Learning system was evaluated with two different datasets, (i) The Early Crop Weeds dataset and (ii) the Plant Seedlings dataset, covering the weeds identification problem. Different configurations, such as the use of plant segmentation, the use of classifier ensembles instead of Softmax and training with noisy data, have been compared. The results showed promising performances of 93.8% and 90.74% F1 score depending on the dataset used. These performances were aligned with other related works in AutoML, but they are far from machine-learning-based systems manually fine-tuned by human experts. From these results, it can be concluded that finding a balance between manual expert work and Automated Machine Learning will be an interesting path to work in order to increase the efficiency in plant protection.

HANDWRITTEN WORD RECOGNITION USING CLASSIFIER ENSEMBLES GENERATED FROM MULTIPLE PROTOTYPES

2004 ◽  
Vol 18 (05) ◽  
pp. 957-974 ◽  
Author(s):  
SIMON GÜNTER ◽  
HORST BUNKE
Keyword(s):  
Handwriting Recognition ◽  
Recognition Rate ◽  
Classifier Ensembles ◽  
Multiple Classifier Systems ◽  
Classification Problems ◽  
Classifier Systems ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Multiple Classifier ◽  
System 1

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. In this paper, we describe our efforts towards improving the performance of state-of-the-art handwriting recognition systems through the use of classifier ensembles. There are many examples of classification problems in the literature where multiple classifier systems increase the performance over single classifiers. Normally one of the two following approaches is used to create a multiple classifier system. (1) Several classifiers are developed completely independent of each other and combined in a last step. (2) Several classifiers are created out of one prototype classifier by using so-called classifier ensemble creation methods. In this paper an algorithm which combines both approaches is introduced and it is used to increase the recognition rate of a hidden Markov model (HMM) based handwritten word recognizer.

scholarly journals Combining multiple algorithms in classifier ensembles using generalized mixture functions

2018 ◽  
Vol 313 ◽  
pp. 402-414 ◽  
Author(s):  
Valdigleis S. Costa ◽  
Antonio Diego S. Farias ◽  
Benjamin Bedregal ◽  
Regivan H.N. Santiago ◽  
Anne Magaly de P. Canuto
Keyword(s):  
Classifier Ensembles ◽  
Multiple Algorithms

Dynamic Weighted Fusion of Adaptive Classifier Ensembles Based on Changing Data Streams

2014 ◽  
pp. 105-116 ◽  
Author(s):  
Christophe Pagano ◽  
Eric Granger ◽  
Robert Sabourin ◽  
Gian Luca Marcialis ◽  
Fabio Roli
Keyword(s):  
Data Streams ◽  
Classifier Ensembles ◽  
Weighted Fusion
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