Personal Attitude Importance Measure

2015 ◽  
Author(s):  
Hsuan-Ting Chen ◽  
Sun Ping ◽  
Gan Chen
Keyword(s):  
Importance Measure ◽  
Personal Attitude ◽  
Attitude Importance

kepemimpinan pendidikan

2020 ◽  
Author(s):  
aprilaukhti
Keyword(s):  
Leadership Style ◽  
Personal Attitude

Style leadership is a someone's characteristics to influence other people or organizations, so others willing and able to move and emulate his personal attitude and character toward penc What purpose. Leadership style is the norm behavior by someone at that time Influence person other.

kepemimpinan pendidikan

2020 ◽  
Author(s):  
aprilaukhti
Keyword(s):  
Leadership Style ◽  
Personal Attitude

Style leadership is a someone's characteristics to influence other people or organizations, so others willing and able to move and emulate his personal attitude and character toward penc What purpose. Leadership style is the norm behavior by someone at that time Influence person other.

scholarly journals How Does the Appeal of Environmental Values Influence Sustainable Entrepreneurial Intention?

2021 ◽  
Vol 18 (3) ◽  
pp. 1070
Author(s):  
Huatao Peng ◽  
Bingbing Li ◽  
Chen Zhou ◽  
Bert M. Sadowski
Keyword(s):  
Self Efficacy ◽  
Structural Equation ◽  
Meta Analysis ◽  
Environmental Values ◽  
Entrepreneurial Intention ◽  
Equation Model ◽  
Entrepreneurial Intentions ◽  
Personal Attitude ◽  
Entrepreneurial Behaviour ◽  
Entrepreneurial Activities

Global challenges posed by climate change and environmental deterioration are increasingly driving entrepreneurship with sustainable entrepreneurial intention as a key driver in predicting entrepreneurial activities. Together with experience, the environmental values of an entrepreneur are vital for sustainable entrepreneurial intention. However, the extent to which experience is a key factor to start up a sustainable enterprise is still rather unclear. To study the role of experience, we derive from the theory of planned behaviour three factors (personal attitude, social norm and self-efficacy) to examine their impact on environmental values and sustainable entrepreneurial intention. Based on a meta-analysis, the overall directions and effect intensity of the different factors in this relationship can be investigated. We develop a structural equation model to explore the mechanism behind the interaction between the different variables. We utilize information from 37 scientific articles using 40 empirical samples, 117 effect sizes and 192,015 observations. We found that environmental values are indeed positively related to a sustainable entrepreneurial intention. Furthermore, the relationship between environmental values and sustainable entrepreneurial intention is moderated by experience, as well as personal attitude, social norms and self-efficacy. In addition, environmental values are more positively related to the intention to set up a sustainable venture for entrepreneurs with low-experience compared to those entrepreneurs with high-experience. For policy makers and managers, it becomes important to stimulate environmental values to promote sustainable entrepreneurial intentions in order to stimulate the growth of sustainable enterprises. By enhancing these three factors, sustainable entrepreneurial behaviour can be facilitated by increasing entrepreneurs’ sustainable intention.

scholarly journals Random forest classification for predicting lifespan-extending chemical compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sofia Kapsiani ◽  
Brendan J. Howlin
Keyword(s):  
Caenorhabditis Elegans ◽  
Random Forest ◽  
Molecular Descriptors ◽  
Area Under The Curve ◽  
Chemical Compounds ◽  
Research Area ◽  
Importance Measure ◽  
Random Forest Algorithm ◽  
Molecular Fingerprints ◽  
Age Related

AbstractAgeing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.

scholarly journals Surrogate minimal depth as an importance measure for variables in random forests

2019 ◽  
Vol 35 (19) ◽  
pp. 3663-3671 ◽  
Author(s):  
Stephan Seifert ◽  
Sven Gundlach ◽  
Silke Szymczak
Keyword(s):  
Variable Selection ◽  
Supplementary Information ◽  
Predictor Variables ◽  
Importance Measure ◽  
Surrogate Variables ◽  
Machine Learning Approach ◽  
Complex Relationships ◽  
Minimal Depth ◽  
Insight Into ◽  
Causal Variables

Abstract Motivation It has been shown that the machine learning approach random forest can be successfully applied to omics data, such as gene expression data, for classification or regression and to select variables that are important for prediction. However, the complex relationships between predictor variables, in particular between causal predictor variables, make the interpretation of currently applied variable selection techniques difficult. Results Here we propose a new variable selection approach called surrogate minimal depth (SMD) that incorporates surrogate variables into the concept of minimal depth (MD) variable importance. Applying SMD, we show that simulated correlation patterns can be reconstructed and that the increased consideration of variable relationships improves variable selection. When compared with existing state-of-the-art methods and MD, SMD has higher empirical power to identify causal variables while the resulting variable lists are equally stable. In conclusion, SMD is a promising approach to get more insight into the complex interplay of predictor variables and outcome in a high-dimensional data setting. Availability and implementation https://github.com/StephanSeifert/SurrogateMinimalDepth. Supplementary information Supplementary data are available at Bioinformatics online.

A Novel Sparse Weighted Least Squares Support Vector Classifier

2013 ◽  
Vol 842 ◽  
pp. 746-749
Author(s):  
Bo Yang ◽  
Liang Zhang
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Importance Measure ◽  
Support Vector ◽  
Comparative Experiment ◽  
Digit Recognition ◽  
Calculating Method ◽  
Imbalance Problem ◽  
Support Vector Classifier

A novel sparse weighted LSSVM classifier is proposed in this paper, which is based on Suykens weighted LSSVM. Unlike Suykens weighted LSSVM, the proposed weighted method is more suitable for classification. The distance between sample and classification border is used as the sample importance measure in our weighted method. Based on this importance measure, a new weight calculating function, using which can adjust the sparseness of weight, is designed. In order to solve the imbalance problem, a kind of normalization weights calculating method is proposed. Finally, the proposed method is used on digit recognition. Comparative experiment results show that the proposed sparse weighted LSSVM can improve the recognition correct rate effectively.

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