Probability Forecasts and Their Combination: A Research Perspective

2019 ◽  
Vol 16 (4) ◽  
pp. 239-260 ◽  
Author(s):  
Robert L. Winkler ◽  
Yael Grushka-Cockayne ◽  
Kenneth C. Lichtendahl ◽  
Victor Richmond R. Jose
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
State Of The Art ◽  
Research Perspective ◽  
Recent Developments ◽  
The Media ◽  
Evaluation Measure ◽  
Probability Forecasts ◽  
Evaluating Forecasts ◽  
Selection Of

We explore some recent, and not so recent, developments concerning the use of probability forecasts and their combination in decision making. Despite these advances, challenges still exist. We expand on some important challenges influencing the “goodness” of combined probability forecasts such as miscalibration, dependence among forecasters, and selection of an appropriate evaluation measure while connecting the processes of aggregating and evaluating forecasts to decision making. Through three important applications from the domains of meteorology, economics, and political science, we illustrate state-of-the-art usage of probability forecasts: how they are combined, evaluated, and communicated to stakeholders. We expect to see greater use and aggregation of probability forecasts, especially given developments in statistical modeling, machine learning, and expert forecasting; the popularity of forecasting competitions; and the increased reporting of probabilities in the media. Our vision is that increased exposure to and improved visualizations of probability forecasts will enhance the public’s understanding of probabilities and how they can contribute to better decisions.

scholarly journals State of the Art in Computational Bioacoustics and Machine Learning: How far have we come?

2019 ◽  
Vol 3 ◽  
Author(s):  
Dan Stowell
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
State Of The Art ◽  
Learning Networks ◽  
Recent Developments ◽  
Audio Data ◽  
Ecological Applications ◽  
Near Future ◽  
Statistical Ecology

Terrestrial bioacoustics, like many other domains, has recently witnessed some transformative results from the application of deep learning and big data (Stowell 2017, Mac Aodha et al. 2018, Fairbrass et al. 2018, Mercado III and Sturdy 2017). Generalising over specific projects, which bioacoustic tasks can we consider "solved"? What can we expect in the near future, and what remains hard to do? What does a bioacoustician need to understand about deep learning? This contribution will address these questions, giving the audience a concise summary of recent developments and ways forward. It builds on recent projects and evaluation campaigns led by the author (Stowell et al. 2015, Stowell et al. 2018), as well as broader developments in signal processing, machine learning and bioacoustic applications of these. We will discuss which type of deep learning networks are appropriate for audio data, how to address zoological/ecological applications which often have few available data, and issues in integrating deep learning predictions with existing workflows in statistical ecology.

scholarly journals Spam Review Detection:A Systematic Literature Review

2020 ◽  
Author(s):  
Muhammad Shoaib Farooq
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
State Of The Art ◽  
The State ◽  
Research Gaps ◽  
Art Research ◽  
Fake Reviews ◽  
Research Studies

In this era of technology, people rely on online posted reviews before buying any product. These reviews are very important for both the consumers and people. Consumers and people use this information for decision making while buying products or investing money in any product. This has inclined the spammers to generate spam or fake reviews so that they can recommend their products and beat the competitors. Spammers have developed many systems to generate the bulk of spam reviews within hours. Many techniques, strategies have been designed and recommended to resolve the issue of spam reviews. In this paper, a complete review of existing techniques and strategies for detecting spam review is discussed. Apart from reviewing the state-of-the-art research studies on spam review detection, a taxonomy on techniques of machine learning for spam review detection has been proposed. Moreover, its focus on research gaps and future recommendations for spam review identification.

COMBINATION, COOPERATION AND SELECTION OF CLASSIFIERS: A STATE OF THE ART

2003 ◽  
Vol 17 (08) ◽  
pp. 1303-1324 ◽  
Author(s):  
V. GUNES ◽  
M. MÉNARD ◽  
P. LOONIS ◽  
S. PETIT-RENAUD
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
State Of The Art ◽  
The Third ◽  
Combination Of Classifiers ◽  
Temporal Aspect ◽  
Selection Of

When several classifiers are brought to contribute to the same task of recognition, various strategies of decisions, implying these classifiers in different ways, are possible. A first strategy consists in deciding using different opinions: it corresponds to the combination of classifiers. A second strategy consists in using one or more opinions for better guiding other classifiers in their training stages, and/or to improve the decision-making of other classifiers in the classification stage: it corresponds to the cooperation of classifiers. The third and last strategy consists in giving more importance to one or more classifiers according to various criteria or situations: it corresponds to the selection of classifiers. The temporal aspect of Pattern Recognition (PR), i.e. the possible evolution of the classes to be recognized, can be treated by the strategy of selection.

scholarly journals Advances and visions in large-scale hydrological modelling: findings from the 11th Workshop on Large-Scale Hydrological Modelling

2008 ◽  
Vol 18 ◽  
pp. 51-61 ◽  
Author(s):  
P. Döll ◽  
K. Berkhoff ◽  
H. Bormann ◽  
N. Fohrer ◽  
D. Gerten ◽  
...  
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Research Area ◽  
Hydrological Modelling ◽  
Integrated Modelling ◽  
Wide Spread ◽  
Research Perspectives ◽  
Recent Developments ◽  
German Speaking ◽  
Selection Of

Abstract. Large-scale hydrological modelling has become increasingly wide-spread during the last decade. An annual workshop series on large-scale hydrological modelling has provided, since 1997, a forum to the German-speaking community for discussing recent developments and achievements in this research area. In this paper we present the findings from the 2007 workshop which focused on advances and visions in large-scale hydrological modelling. We identify the state of the art, difficulties and research perspectives with respect to the themes "sensitivity of model results", "integrated modelling" and "coupling of processes in hydrosphere, atmosphere and biosphere". Some achievements in large-scale hydrological modelling during the last ten years are presented together with a selection of remaining challenges for the future.

scholarly journals DeePaC: Predicting pathogenic potential of novel DNA with a universal framework for reverse-complement neural networks

2019 ◽  
Author(s):  
Jakub M. Bartoszewicz ◽  
Anja Seidel ◽  
Robert Rentzsch ◽  
Bernhard Y. Renard
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Pathogen Detection ◽  
State Of The Art ◽  
Model Performance ◽  
Pathogenic Potential ◽  
Reverse Complement ◽  
Recent Developments ◽  
Undesirable Property ◽  
Simple Character

AbstractMotivation:We expect novel pathogens to arise due to their fast-paced evolution, and new species to be discovered thanks to advances in DNA sequencing and metagenomics. What is more, recent developments in synthetic biology raise concerns that some strains of bacteria could be modified for malicious purposes. Traditional approaches to open-view pathogen detection depend on databases of known organisms, limiting their performance on unknown, unrecognized, and unmapped sequences. In contrast, machine learning methods can infer pathogenic phenotypes from single NGS reads even though the biological context is unavailable. However, modern neural architectures treat DNA as a simple character string and may predict conflicting labels for a given sequence and its reverse-complement. This undesirable property may impact model performance.Results:We present DeePaC, a Deep Learning Approach to Pathogenicity Classification. It includes a universal, extensible framework for neural architectures ensuring identical predictions for any given DNA sequence and its reverse-complement. We implement reverse-complement convolutional neural networks and LSTMs, which outperform the state-of-the-art methods based on both sequence homology and machine learning. Combining a reverse-complement architecture with integrating the predictions for both mates in a read pair results in cutting the error rate almost in half in comparison to the previous state-of-the-art.Availability:The code and the models are available at: https://gitlab.com/rki_bioinformatics/DeePaC

scholarly journals Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 473
Author(s):  
Patrik Zajec ◽  
Jože M. Rožanec ◽  
Elena Trajkova ◽  
Inna Novalija ◽  
Klemen Kenda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Active Learning ◽  
Demand Forecasting ◽  
Research Work ◽  
Use Case ◽  
Multiple Strategies ◽  
Machine Learning Model ◽  
The Media ◽  
Gathering Data

This research work describes an architecture for building a system that guides a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in a manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides the means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. We compare such strategies through a set of experiments to understand how they balance learning and provide accurate media news recommendations to the user. The media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.

scholarly journals A Survey on the Explainability of Supervised Machine Learning

2021 ◽  
Vol 70 ◽  
pp. 245-317
Author(s):  
Nadia Burkart ◽  
Marco F. Huber
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
State Of The Art ◽  
High Accuracy ◽  
Supervised Machine Learning ◽  
Future Directions ◽  
Survey Paper ◽  
Black Boxes ◽  
Highly Sensitive

Predictions obtained by, e.g., artificial neural networks have a high accuracy but humans often perceive the models as black boxes. Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or finance, is of paramount importance. The decision-making behind the black boxes requires it to be more transparent, accountable, and understandable for humans. This survey paper provides essential definitions, an overview of the different principles and methodologies of explainable Supervised Machine Learning (SML). We conduct a state-of-the-art survey that reviews past and recent explainable SML approaches and classifies them according to the introduced definitions. Finally, we illustrate principles by means of an explanatory case study and discuss important future directions.

Sign in / Sign up

Export Citation Format

Share Document