scholarly journals OVERSAMPLING METHOD TO HANDLING IMBALANCED DATASETS PROBLEM IN BINARY LOGISTIC REGRESSION ALGORITHM

2020 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Windyaning Ustyannie ◽  
S Suprapto
Keyword(s):  
Logistic Regression ◽  
Sampling Method ◽  
Synthetic Data ◽  
Class Imbalance ◽  
Binary Logistic Regression ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Logistic Regression Method ◽  
Increase In Accuracy ◽  
Logistic Regression Algorithm

The class imbalance is a condition when one class has a higher percentage than the other then it can affect the accuracy. One method in data mining that can be used to classification is logistic regression method. The method used in this research is RWO-sampling method using random replicate approach for synthetic data generation on descrete attribute. The result of the research can handle the problem of class imbalance, RWO-sampling method with random replicate approach shows better accuracy than RWO-sampling method with roulette and ROS approach. The accuracy value for RWO-Sampling method with roulette and RWO-Sampling approach with random replicate approach has increased to an average of 15.55% of each dataset. As for comparithem with the ROS method has increased an average of 3.7% of each dataset. Furthermore, for testing the underfitting problem in logistic regression, the oversampling method is better than non-oversampling with an increase in accuracy value reaching an average of 2.3% of each dataset.

Synthetic data generation of high-resolution hyperspectral data using DIRSIG

2007 ◽  
Author(s):  
Marek K. Jakubowski ◽  
David Pogorzala ◽  
Timothy J. Hattenberger ◽  
Scott D. Brown ◽  
John R. Schott
Keyword(s):  
High Resolution ◽  
Synthetic Data ◽  
Hyperspectral Data ◽  
Data Generation ◽  
Synthetic Data Generation

Synthetic Data Generation to Support Irregular Sampling in Sensor Networks

2004 ◽  
pp. 211-234 ◽  
Author(s):  
Lewis Girod ◽  
Ramesh Govindan ◽  
Deepak Ganesan ◽  
Deborah Estrin ◽  
Yan Yu
Keyword(s):  
Sensor Networks ◽  
Synthetic Data ◽  
Irregular Sampling ◽  
Data Generation ◽  
Synthetic Data Generation

scholarly journals Instance Segmentation in CARLA: Methodology and Analysis for Pedestrian-oriented Synthetic Data Generation in Crowded Scenes

2021 ◽  
Author(s):  
Maria Lyssenko ◽  
Christoph Gladisch ◽  
Christian Heinzemann ◽  
Matthias Woehrle ◽  
Rudolph Triebel
Keyword(s):  
Synthetic Data ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Crowded Scenes ◽  
Instance Segmentation

scholarly journals Faktor Keberhasilan Usaha UMKM Jajanan Asing Kaki Lima di Kota Serang

2018 ◽  
Vol 12 (2) ◽  
pp. 187
Author(s):  
Fauzan Anggi Prasatya ◽  
Tjahja Muhandri ◽  
Eko Ruddy Cahyadi
Keyword(s):  
Logistic Regression ◽  
Analytical Method ◽  
Success Factors ◽  
Sampling Method ◽  
Descriptive Analysis ◽  
Binary Logistic Regression ◽  
Success Factor ◽  
Street Food ◽  
Product Innovations ◽  
Start Up

The competition of food business is currently very strict and diverse product innovations. To achieve the market share and win the business competition needs to know the affecting success factors. This study has two main objectives that include the following to: (1) mapping the characteristics of non traditional street food entrepreneur in Serang City, (2) identify the most affected success factor of non traditional street food business. Sampling method was used by purposive sampling 100 respondents. The analytical method used descriptive analysis and binary logistic regression. This research showed most of successful vendor are woman, because they are very conscientious than mens and tend to avoid risk. Affecting success factors on non traditional street food business were price of the product, business name and start up capital.

Synthetic Data Generation Capabilties for Testing Data Mining Tools

MILCOM 2006 ◽  
2006 ◽  
Author(s):  
Daniel Jeske ◽  
Pengyue Lin ◽  
Carlos Rendon ◽  
Rui Xiao ◽  
Behrokh Samadi
Keyword(s):  
Data Mining ◽  
Synthetic Data ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Testing Data ◽  
Mining Tools

scholarly journals Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs

2019 ◽  
Vol 30 (3) ◽  
pp. 627-648 ◽  
Author(s):  
Evelyn Buckwar ◽  
Massimiliano Tamborrino ◽  
Irene Tubikanec
Keyword(s):  
Numerical Methods ◽  
Spectral Density ◽  
Diffusion Processes ◽  
Model Simulation ◽  
Broad Class ◽  
Synthetic Data ◽  
Summary Statistics ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Partially Observed

Abstract Approximate Bayesian computation (ABC) has become one of the major tools of likelihood-free statistical inference in complex mathematical models. Simultaneously, stochastic differential equations (SDEs) have developed to an established tool for modelling time-dependent, real-world phenomena with underlying random effects. When applying ABC to stochastic models, two major difficulties arise: First, the derivation of effective summary statistics and proper distances is particularly challenging, since simulations from the stochastic process under the same parameter configuration result in different trajectories. Second, exact simulation schemes to generate trajectories from the stochastic model are rarely available, requiring the derivation of suitable numerical methods for the synthetic data generation. To obtain summaries that are less sensitive to the intrinsic stochasticity of the model, we propose to build up the statistical method (e.g. the choice of the summary statistics) on the underlying structural properties of the model. Here, we focus on the existence of an invariant measure and we map the data to their estimated invariant density and invariant spectral density. Then, to ensure that these model properties are kept in the synthetic data generation, we adopt measure-preserving numerical splitting schemes. The derived property-based and measure-preserving ABC method is illustrated on the broad class of partially observed Hamiltonian type SDEs, both with simulated data and with real electroencephalography data. The derived summaries are particularly robust to the model simulation, and this fact, combined with the proposed reliable numerical scheme, yields accurate ABC inference. In contrast, the inference returned using standard numerical methods (Euler–Maruyama discretisation) fails. The proposed ingredients can be incorporated into any type of ABC algorithm and directly applied to all SDEs that are characterised by an invariant distribution and for which a measure-preserving numerical method can be derived.

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