scholarly journals Conservative Policy Construction Using Variational Autoencoders for Logged Data With Missing Values

2022 ◽  
pp. 1-11
Author(s):  
Mahed Abroshan ◽  
Kai Hou Yip ◽  
Cem Tekin ◽  
Mihaela van der Schaar
Keyword(s):  
Missing Values ◽  
Policy Construction ◽  
Conservative Policy

THE GENESIS OF CONSERVATIVE IDEOLOGY, DEVELOPMENT TRENDS AND PROSPECTS IN THE POLITICAL REFORM OF MODERN COUNTRIES (ON THE EXAMPLE OF RUSSIA AND FRANCE)

2020 ◽  
Vol 10 (4) ◽  
pp. 38-43
Author(s):  
MARIETA EPREMYAN ◽  
Keyword(s):  
Political Reform ◽  
Point Of View ◽  
The Political ◽  
Development Trends ◽  
Conservative Ideology ◽  
The World ◽  
Christian Conservatism ◽  
The Future ◽  
Conservative Policy ◽  
Depth Interviews

The article examines the epistemological roots of conservative ideology, development trends and further prospects in political reform not only in modern Russia, but also in other countries. The author focuses on the “world” and Russian conservatism. In the course of the study, the author illustrates what opportunities and limitations a conservative ideology can have in political reform not only in modern Russia, but also in the world. In conclusion, it is concluded that the prospect of a conservative trend in the world is wide enough. To avoid immigration and to control the development of technology in society, it is necessary to adhere to a conservative policy. Conservatism is a consolidating ideology. It is no coincidence that the author cites as an example the understanding of conservative ideology by the French due to the fact that Russia has its own vision of the ideology of conservatism. If we say that conservatism seeks to preserve something and respects tradition, we must bear in mind that traditions in different societies, which form some kind of moral imperatives, cannot be a single phenomenon due to different historical destinies and differing religious views. Considered from the point of view of religion, Muslim and Christian conservatism will be somewhat confrontational on some issues. The purpose of the work was to consider issues related to the role, evolution and prospects of conservative ideology in the political reform of modern countries. The author focuses on Russia and France. To achieve this goal, the method of in-depth interviews with experts on how they understand conservatism was chosen. Already today, conservatism is quite diverse. It is quite possible that in the future it will transform even more and acquire new reflections.

Missing Data - Better "Not to Have Them", but What If You Do? (Part 1)

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 21-32
Author(s):  
Dirk Temme ◽  
Sarah Jensen
Keyword(s):  
Missing Data ◽  
Statistical Power ◽  
Missing Values ◽  
Graphical Representation ◽  
Marketing Research ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Full Information Maximum Likelihood ◽  
Definition Of ◽  
Traditional Approaches

Missing values are ubiquitous in empirical marketing research. If missing data are not dealt with properly, this can lead to a loss of statistical power and distorted parameter estimates. While traditional approaches for handling missing data (e.g., listwise deletion) are still widely used, researchers can nowadays choose among various advanced techniques such as multiple imputation analysis or full-information maximum likelihood estimation. Due to the available software, using these modern missing data methods does not pose a major obstacle. Still, their application requires a sound understanding of the prerequisites and limitations of these methods as well as a deeper understanding of the processes that have led to missing values in an empirical study. This article is Part 1 and first introduces Rubin’s classical definition of missing data mechanisms and an alternative, variable-based taxonomy, which provides a graphical representation. Secondly, a selection of visualization tools available in different R packages for the description and exploration of missing data structures is presented.

How toxicity of nanomaterials towards different species could be simultaneously evaluated: Novel multi-nano-read-across approach

2017 ◽  
Author(s):  
Natalia Sizochenko ◽  
Alicja Mikolajczyk ◽  
Karolina Jagiello ◽  
Tomasz Puzyn ◽  
Jerzy Leszczynski ◽  
...  
Keyword(s):  
Missing Values ◽  
Metal Oxide Nanoparticles ◽  
Machine Learning Techniques ◽  
Self Organizing Map ◽  
Related Data ◽  
Mammalian Cell Lines ◽  
Learning Techniques ◽  
Combination Of Methods ◽  
Map Analysis ◽  
Unique Chemical

Application of predictive modeling approaches is able solve the problem of the missing data. There are a lot of studies that investigate the effects of missing values on qualitative or quantitative modeling, but only few publications have been<br>discussing it in case of applications to nanotechnology related data. Current project aimed at the development of multi-nano-read-across modeling technique that helps in predicting the toxicity of different species: bacteria, algae, protozoa, and mammalian cell lines. In this study, the experimental toxicity for 184 metal- and silica oxides (30 unique chemical types) nanoparticles from 15 experimental datasets was analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis was developed. At the first step, hidden patterns of toxicity among the nanoparticles were identified using a combination of methods. Then the developed model that based on categorization of metal oxide nanoparticles’ toxicity outcomes was evaluated by means of combination of supervised and unsupervised machine learning techniques to find underlying factors responsible for toxicity.

How toxicity of nanomaterials towards different species could be simultaneously evaluated: Novel multi-nano-read-across approach

2017 ◽  
Author(s):  
Natalia Sizochenko ◽  
Alicja Mikolajczyk ◽  
Karolina Jagiello ◽  
Tomasz Puzyn ◽  
Jerzy Leszczynski ◽  
...  
Keyword(s):  
Missing Values ◽  
Metal Oxide Nanoparticles ◽  
Machine Learning Techniques ◽  
Self Organizing Map ◽  
Related Data ◽  
Mammalian Cell Lines ◽  
Learning Techniques ◽  
Combination Of Methods ◽  
Map Analysis ◽  
Unique Chemical

Application of predictive modeling approaches is able solve the problem of the missing data. There are a lot of studies that investigate the effects of missing values on qualitative or quantitative modeling, but only few publications have been<br>discussing it in case of applications to nanotechnology related data. Current project aimed at the development of multi-nano-read-across modeling technique that helps in predicting the toxicity of different species: bacteria, algae, protozoa, and mammalian cell lines. In this study, the experimental toxicity for 184 metal- and silica oxides (30 unique chemical types) nanoparticles from 15 experimental datasets was analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis was developed. At the first step, hidden patterns of toxicity among the nanoparticles were identified using a combination of methods. Then the developed model that based on categorization of metal oxide nanoparticles’ toxicity outcomes was evaluated by means of combination of supervised and unsupervised machine learning techniques to find underlying factors responsible for toxicity.

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.

2020 ◽  
Vol 21 ◽  
Author(s):  
Sukanya Panja ◽  
Sarra Rahem ◽  
Cassandra J. Chu ◽  
Antonina Mitrofanova
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Therapeutic Response ◽  
Patient Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Human Machine Interaction ◽  
Data Repositories ◽  
Response Modeling

Background: In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design. Objective: In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches all in light of their application to therapeutic response modeling in cancer. Conclusion: We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.

MATHURA (MBI) - A NOVEL IMPUTATION MEASURE FOR IMPUTATION OF MISSING VALUES IN MEDICAL DATASETS

2019 ◽  
Vol 13 ◽  
Author(s):  
B. Mathura Bai ◽  
N. Mangathayaru ◽  
B. Padmaja Rani ◽  
Shadi Aljawarneh
Keyword(s):  
Similarity Measure ◽  
Medical Records ◽  
Missing Values ◽  
Similarity Measures ◽  
Common Problems ◽  
Experiment Analysis

: Missing attribute values in medical datasets are one of the most common problems faced when mining medical datasets. Estimation of missing values is a major challenging task in pre-processing of datasets. Any wrong estimate of missing attribute values can lead to inefficient and improper classification thus resulting in lower classifier accuracies. Similarity measures play a key role during the imputation process. The use of an appropriate and better similarity measure can help to achieve better imputation and improved classification accuracies. This paper proposes a novel imputation measure for finding similarity between missing and non-missing instances in medical datasets. Experiments are carried by applying both the proposed imputation technique and popular benchmark existing imputation techniques. Classification is carried using KNN, J48, SMO and RBFN classifiers. Experiment analysis proved that after imputation of medical records using proposed imputation technique, the resulting classification accuracies reported by the classifiers KNN, J48 and SMO have improved when compared to other existing benchmark imputation techniques.

Direct Democracy and Public Policy

2019 ◽  
pp. 362-379
Author(s):  
John Matsusaka
Keyword(s):  
Public Policy ◽  
Direct Democracy ◽  
Median Voter ◽  
Extensive Literature ◽  
Fiscal Policies ◽  
Policy Choices ◽  
American States ◽  
Conservative Policy ◽  
Initiative Process ◽  
Swiss Cantons

An extensive literature seeks to measure the effect of the initiative and referendum on public policies. Several conclusions emerge: The initiative and referendum have different effects on policy. Requiring popular approval of fiscal policies (mandatory referendums) results in lower expenditure, taxes, and debt. The initiative process gives voters more power and results in policies closer to the median voter preference; this often has reduced spending (American states, Swiss cantons), but sometimes has increased spending (cities). The initiative is associated with more socially conservative policy choices. Spurious correlation is a concern in many studies, and more research on causality is needed.

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