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Wyświetlanie 1-2 z 2
Tytuł:
Utilizing decision trees on employee decision-making processes: a model proposal
Autorzy:
Yüncü, Volkan
Fidan, Üzeyir
Powiązania:
https://bibliotekanauki.pl/articles/2033057.pdf
Data publikacji:
2021
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
perceived organizational support
employee silence
decision tree
HRM
postrzegane wsparcie organizacyjne
milczenie pracowników
drzewo decyzyjne
Opis:
Introduction/background: This paper offers an idiosyncratic relational framework built on the organizational silence theory and the organizational support theory. It exploits the distinct advantages that using decision trees in classification and prediction applications offer to form a unique predictive model. Aim of the paper: This paper argues that a relational framework built on the organizational silence theory and the organizational support theory can give important clues about how employees make certain decisions in the workplace as well as about factors that have an impact on their decision-making processes. Materials and methods: The research applies decision trees learning – a data mining technique – to unfold the hidden patterns and unprecedented relationships between the two constructs that until now had not been revealed. Results and conclusions: The suggested model, which consists of rules, exhibits the effects of perceived organizational support and employee silence behavior on employee decisions with an approximately 79% correct classification rate, showing the success of the model as well as its appropriate relational framework. The presented findings indicate that a relational framework built on the organizational silence theory and the organizational support theory has a lot to offer in terms of building effective HR strategies and policies. The study also extends the understanding of the antecedents of silence behavior in different social contexts.
Źródło:
Organizacja i Zarządzanie : kwartalnik naukowy; 2020, nr 3; 137-152
1899-6116
Pojawia się w:
Organizacja i Zarządzanie : kwartalnik naukowy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Imitation learning of car driving skills with decision trees and random forests
Autorzy:
Cichosz, P.
Pawełczak, Ł.
Powiązania:
https://bibliotekanauki.pl/articles/329901.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
imitation learning
behavioral cloning
model ensemble
random forest
control
autonomous driving
car racing
decision tree
drzewo decyzyjne
lasy losowe
sterowanie
wyścigi samochodowe
Opis:
Machine learning is an appealing and useful approach to creating vehicle control algorithms, both for simulated and real vehicles. One common learning scenario that is often possible to apply is learning by imitation, in which the behavior of an exemplary driver provides training instances for a supervised learning algorithm. This article follows this approach in the domain of simulated car racing, using the TORCS simulator. In contrast to most prior work on imitation learning, a symbolic decision tree knowledge representation is adopted, which combines potentially high accuracy with human readability, an advantage that can be important in many applications. Decision trees are demonstrated to be capable of representing high quality control models, reaching the performance level of sophisticated pre-designed algorithms. This is achieved by enhancing the basic imitation learning scenario to include active retraining, automatically triggered on control failures. It is also demonstrated how better stability and generalization can be achieved by sacrificing human-readability and using decision tree model ensembles. The methodology for learning control models contributed by this article can be hopefully applied to solve real-world control tasks, as well as to develop video game bots.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 579-597
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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