A probabilistic modeling approach for interpretable data inference and classification

2020 ◽  
pp. 1-17
Author(s):  
Shuaiyu Yao ◽  
Jian-Bo Yang ◽  
Dong-Ling Xu
Keyword(s):  
Statistical Analysis ◽  
Probabilistic Modeling ◽  
Data Set ◽  
Inference Mechanism ◽  
Modeling Approach ◽  
Input Variables ◽  
Data Inference ◽  
The Relationship ◽  
Better Than ◽  
Iris Data

In this paper, we propose a new probabilistic modeling approach for interpretable inference and classification using the maximum likelihood evidential reasoning (MAKER) framework. This approach integrates statistical analysis, hybrid evidence combination and belief rule-based (BRB) inference, and machine learning. Statistical analysis is used to acquire evidence from data. The BRB inference is applied to analyze the relationship between system inputs and outputs. An interdependence index is used to quantify the interdependence between input variables. An adapted genetic algorithm is applied to train the models. The model established by the approach features a unique strong interpretability, which is reflected in three aspects: (1) interpretable evidence acquisition, (2) interpretable inference mechanism, and (3) interpretable parameters determination. The MAKER-based model is shown to be a competitive classifier for the Banana, Haberman’s survival, and Iris data set, and generally performs better than other interpretable classifiers, e.g., complex tree, logistic regression, and naive Bayes.

An improvement to the ANN-enhanced flow rating method

2021 ◽  
Author(s):  
Bernhard Schmid
Keyword(s):  
Neural Network ◽  
Electrical Conductivity ◽  
Water Resources ◽  
Flow Rate ◽  
Major Ions ◽  
Rate Of Change ◽  
Data Set ◽  
Input Variables ◽  
Relationship Of ◽  
The Relationship

<p>The work reported here builds upon a previous pilot study by the author on ANN-enhanced flow rating (Schmid, 2020), which explored the use of electrical conductivity (EC) in addition to stage to obtain ‘better’, i.e. more accurate and robust, estimates of streamflow. The inclusion of EC has an advantage, when the relationship of EC versus flow rate is not chemostatic in character. In the majority of cases, EC is, indeed, not chemostatic, but tends to decrease with increasing discharge (so-called dilution behaviour), as reported by e.g. Moatar et al. (2017), Weijs et al. (2013) and Tunqui Neira et al.(2020). This is also in line with this author’s experience.</p><p>The research presented here takes the neural network based approach one major step further and incorporates the temporal rate of change in stage and the direction of change in EC among the input variables (which, thus, comprise stage, EC, change in stage and direction of change in EC). Consequently, there are now 4 input variables in total employed as predictors of flow rate. Information on the temporal changes in both flow rate and EC helps the Artificial Neural Network (ANN) characterize hysteretic behaviour, with EC assuming different values for falling and rising flow rate, respectively, as described, for instance, by Singley et al. (2017).</p><p>The ANN employed is of the Multilayer Perceptron (MLP) type, with stage, EC, change in stage and direction of change in EC of the Mödling data set (Schmid, 2020) as input variables. Summarising the stream characteristics, the Mödling brook can be described as a small Austrian stream with a catchment of fairly mixed composition (forests, agricultural and urbanized areas). The relationship of EC versus flow reflects dilution behaviour. Neural network configuration 4-5-1 (the 4 input variables mentioned above, 5 hidden nodes and discharge as the single output) with learning rate 0.05 and momentum 0.15 was found to perform best, with testing average RMSE (root mean square error) of the scaled output after 100,000 epochs amounting to 0.0138 as compared to 0.0216 for the (best performing) 2-5-1 MLP with stage and EC as inputs only.    </p><p> </p><p>References</p><p>Moatar, F., Abbott, B.W., Minaudo, C., Curie, F. and Pinay, G.: Elemental properties, hydrology, and biology interact to shape concentration-discharge curves for carbon, nutrients, sediment and major ions. Water Resources Res., 53, 1270-1287, 2017.</p><p>Schmid, B.H.: Enhanced flow rating using neural networks with water stage and electrical conductivity as predictors. EGU2020-1804, EGU General Assembly 2020.</p><p>Singley, J.G., Wlostowski, A.N., Bergstrom, A.J., Sokol, E.R., Torrens, C.L., Jaros, C., Wilson, C.,E., Hendrickson, P.J. and Gooseff, M.N.: Characterizing hyporheic exchange processes using high-frequency electrical conductivity-discharge relationships on subhourly to interannual timescales. Water Resources Res. 53, 4124-4141, 2017.</p><p>Tunqui Neira, J.M., Andréassian, V., Tallec, G. and Mouchel, J.-M.: A two-sided affine power scaling relationship to represent the concentration-discharge relationship. Hydrol. Earth Syst. Sci. 24, 1823-1830, 2020.</p><p>Weijs, S.V., Mutzner, R. and Parlange, M.B.: Could electrical conductivity replace water level in rating curves for alpine streams? Water Resources Research 49, 343-351, 2013.</p>

scholarly journals Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data

2019 ◽  
Vol 11 (15) ◽  
pp. 1837 ◽  
Author(s):  
James Brinkhoff ◽  
Brian W. Dunn ◽  
Andrew J. Robson ◽  
Tina S. Dunn ◽  
Remy L. Dehaan
Keyword(s):  
Vegetation Index ◽  
Field Experiments ◽  
N Uptake ◽  
Satellite Image ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Data Set ◽  
Multispectral Data ◽  
Input Variables ◽  
Better Than

Mid-season nitrogen (N) application in rice crops can maximize yield and profitability. This requires accurate and efficient methods of determining rice N uptake in order to prescribe optimal N amounts for topdressing. This study aims to determine the accuracy of using remotely sensed multispectral data from satellites to predict N uptake of rice at the panicle initiation (PI) growth stage, with a view to providing optimum variable-rate N topdressing prescriptions without needing physical sampling. Field experiments over 4 years, 4–6 N rates, 4 varieties and 2 sites were conducted, with at least 3 replicates of each plot. One WorldView satellite image for each year was acquired, close to the date of PI. Numerous single- and multi-variable models were investigated. Among single-variable models, the square of the NDRE vegetation index was shown to be a good predictor of N uptake (R 2 = 0.75, RMSE = 22.8 kg/ha for data pooled from all years and experiments). For multi-variable models, Lasso regularization was used to ensure an interpretable and compact model was chosen and to avoid over fitting. Combinations of remotely sensed reflectances and spectral indexes as well as variety, climate and management data as input variables for model training achieved R 2 < 0.9 and RMSE < 15 kg/ha for the pooled data set. The ability of remotely sensed data to predict N uptake in new seasons where no physical sample data has yet been obtained was tested. A methodology to extract models that generalize well to new seasons was developed, avoiding model overfitting. Lasso regularization selected four or less input variables, and yielded R 2 of better than 0.67 and RMSE better than 27.4 kg/ha over four test seasons that weren’t used to train the models.

Post M&A innovation in family firms

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abdul-Basit Issah
Keyword(s):  
Design Methodology ◽  
Family Firms ◽  
Empirical Support ◽  
Data Set ◽  
Resource Based View ◽  
Content Type ◽  
External Resources ◽  
Merger Wave ◽  
The Relationship ◽  
Better Than

PurposeThe paper empirically investigates how family firms appropriate acquired resources to become more innovative in the context of merger waves. It draws on resource-based view and the theory of first mover (dis)advantages to examine the implications of the timing of acquisitions on innovation in family firms.Design/methodology/approachThe paper uses a panel data set of Standard & Poor's (S&P) 500 manufacturing firms followed over a period of 31 years.FindingsThe study finds empirical support for the predictions that family firms are more able to utilize acquired resources better than nonfamily firms. Furthermore, targets acquired during the upswing of a merger wave are more valuable to family firms and associated with more innovation than for nonfamily firms.Originality/valueThe paper establishes that resources acquired during the upswing of a merger wave are more valuable, provide better resource synergies and impact innovation positively in family firms than nonfamily firms. Second, the paper makes an empirical contribution that family firms absorb external resources markedly differently and more efficiently than nonfamily firms. Third, the paper enhances a better understanding of the influence of family ownership on the relationship between acquisitions and innovation outputs.

scholarly journals RDMMFET: Representation of Dense Multimodality Fusion Encoder Based on Transformer

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xu Zhang ◽  
DeZhi Han ◽  
Chin-Chen Chang
Keyword(s):  
Question Answering ◽  
Language Model ◽  
Visual Images ◽  
Text And Image ◽  
Data Set ◽  
Fine Grained ◽  
Natural Language Question ◽  
The Relationship ◽  
Language Question ◽  
Better Than

Visual question answering (VQA) is the natural language question-answering of visual images. The model of VQA needs to make corresponding answers according to specific questions based on understanding images, the most important of which is to understand the relationship between images and language. Therefore, this paper proposes a new model, Representation of Dense Multimodality Fusion Encoder Based on Transformer, for short, RDMMFET, which can learn the related knowledge between vision and language. The RDMMFET model consists of three parts: dense language encoder, image encoder, and multimodality fusion encoder. In addition, we designed three types of pretraining tasks: masked language model, masked image model, and multimodality fusion task. These pretraining tasks can help to understand the fine-grained alignment between text and image regions. Simulation results on the VQA v2.0 data set show that the RDMMFET model can work better than the previous model. Finally, we conducted detailed ablation studies on the RDMMFET model and provided the results of attention visualization, which proves that the RDMMFET model can significantly improve the effect of VQA.

Deterring Unequally II: Regional Power Nuclear Postures and Crisis Behavior

2014 ◽  
Author(s):  
Vipin Narang
Keyword(s):  
Statistical Analysis ◽  
Nuclear Weapons ◽  
Regional Power ◽  
Conflict Escalation ◽  
Fine Grained ◽  
Crisis Behavior ◽  
The Relationship ◽  
Better Than

This chapter probes questions regarding how nuclear weapons or nuclear postures affect crisis dynamics, by examining whether there is variation in states' decisions to escalate or de-escalate a crisis as a function of nuclear posture. That is, within a crisis, the chapter considers if some nuclear postures deter states from conflict escalation better than others. In answering this question, this chapter uncovers the mechanisms responsible for the relationship between regional nuclear postures and deterrence outcomes, ensuring that the correlations established in the statistical analysis are not just spurious but are real and causal. To do this, the chapter explores the findings from the large-n analysis in more fine-grained crisis settings.

How Dangerous Is Looking Away From the Road? Algorithms Predict Crash Risk From Glance Patterns in Naturalistic Driving

2012 ◽  
Vol 54 (6) ◽  
pp. 1104-1116 ◽  
Author(s):  
Yulan Liang ◽  
John D. Lee ◽  
Lora Yekhshatyan
Keyword(s):  
Motor Vehicle ◽  
Driver Distraction ◽  
Crash Risk ◽  
Design Of Algorithms ◽  
Data Set ◽  
The Road ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
The Relationship ◽  
Better Than

Objective: In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. Background: Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash risk lacks detailed quantification. Method: The authors compared 24 algorithms that varied according to how they incorporated three potential contributors to distraction—glance duration, glance history, and glance location—on how well the algorithms predicted crash risk. Results: Distraction estimated from driver eye-glance patterns was positively associated with crash risk. The algorithms incorporating ongoing off-road glance duration predicted crash risk better than did the algorithms incorporating glance history. Augmenting glance duration with other elements of glance behavior—1.5th power of duration and duration weighted by glance location—produced similar prediction performance as glance duration alone. Conclusions: The distraction level estimated by the algorithms that include current glance duration provides the most sensitive indicator of crash risk. Application: The results inform the design of algorithms to monitor driver state that support real-time distraction mitigation systems.

scholarly journals Modeling of temporal development of population taking into account the situation of municipalities in the resettlement system (according to the example of the Tyumen Region)

2020 ◽  
Vol 26 (1) ◽  
pp. 215-227
Author(s):  
V. Dobryakova ◽  
A. Dobryakov
Keyword(s):  
Regression Analysis ◽  
Statistical Analysis ◽  
Spatial Statistics ◽  
Network Data ◽  
Regression Equations ◽  
Temporal Development ◽  
Data Set ◽  
Regional Center ◽  
Definition Of ◽  
The Relationship

The work is devoted to application of spatial statistics and regression analysis tools in the ArcGIS Pro program. In this report we try to confirm two theories in the relationship between positional characteristics of municipalities and the temporal development of population: The farther the locality is from the main settlement of the territory, the faster it loses its own population. The farther the locality is from the main highways of the territory, the faster it loses its own population. The main aim of this article is to find the strictest definition of the type of correlation between such specific distances as the distance to the regional center, the distance to the nearest highway and the relative changes in the municipalities’ population, according to the example of the Tyumen region. A network data set was created to calculate the distances, it contains several elements: main roads, calculated centers of municipalities (CM), lines — distances from centers to the nearest road (“stops”). For the study we used information on changes of population for 4 periods: 1981–1990, 1990–2002, 2002–2010 and 2010–2018. The dependence was done by enumerating the degrees of distances. We considered that the dependence was selected in case the relevant correlation coefficient was the largest. For each chosen relationship, ArcGIS Pro performed a complete statistical analysis, based on the results, the significance of the model was identified, residual maps constructed, and regression equations calculated. All the models except the first period turned out to be significant, but they were displaced, which indicates the existence of some unexplored factors. In the context of the constructed models, it was assumed that the distance to the regional center is closely connected with an expansion of the population in the surrounding municipalities, but the expansion gets more the closer the municipal district is to Tyumen. The distance to the nearest highway is associated with a decrease of population, and the farther the municipality is from the highway, the more it loses population.

Maintenance of a Genetic Data Set on Multiple Data Base and Statistical Analysis Systems

1982 ◽  
Vol 61 (s109) ◽  
pp. 34-34
Author(s):  
Samuel J. Agronow ◽  
Federico C. Mariona ◽  
Frederick C. Koppitch ◽  
Kazutoshi Mayeda
Keyword(s):  
Statistical Analysis ◽  
Data Base ◽  
Genetic Data ◽  
Data Set ◽  
Multiple Data

Evaluation for estimating of the PDF and the CDF of Generalized Inverted Exponential Distribution with Application in Industry

2020 ◽  
pp. 507-522
Author(s):  
Parisa Torkaman
Keyword(s):  
Least Squares ◽  
Exponential Distribution ◽  
Mean Squared Error ◽  
Weighted Least Squares ◽  
Real Data ◽  
Minimum Variance ◽  
Cumulative Distribution ◽  
Estimation Methods ◽  
Data Set ◽  
Better Than

The generalized inverted exponential distribution is introduced as a lifetime model with good statistical properties. This paper, the estimation of the probability density function and the cumulative distribution function of with five different estimation methods: uniformly minimum variance unbiased(UMVU), maximum likelihood(ML), least squares(LS), weighted least squares (WLS) and percentile(PC) estimators are considered. The performance of these estimation procedures, based on the mean squared error (MSE) by numerical simulations are compared. Simulation studies express that the UMVU estimator performs better than others and when the sample size is large enough the ML and UMVU estimators are almost equivalent and efficient than LS, WLS and PC. Finally, the result using a real data set are analyzed.

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