scholarly journals Non-Intrusive Parametric Audio Quality Estimation Models for Broadcasting Systems and Web-Casting Applications Based on Random Forest

2020 ◽  
Vol 18 (4) ◽  
Author(s):  
Martin Jakubik ◽  
Peter Pocta
Keyword(s):  
Random Forest ◽  
Quality Estimation ◽  
Audio Quality ◽  
Estimation Models

scholarly journals Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Ju-Young Shin ◽  
Yonghun Ro ◽  
Joo-Wan Cha ◽  
Kyu-Rang Kim ◽  
Jong-Chul Ha
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
South Korea ◽  
Extreme Learning Machine ◽  
Radar Data ◽  
Machine Learning Algorithms ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Learning Machine ◽  
Estimation Models

Machine learning algorithms should be tested for use in quantitative precipitation estimation models of rain radar data in South Korea because such an application can provide a more accurate estimate of rainfall than the conventional ZR relationship-based model. The applicability of random forest, stochastic gradient boosted model, and extreme learning machine methods to quantitative precipitation estimation models was investigated using case studies with polarization radar data from Gwangdeoksan radar station. Various combinations of input variable sets were tested, and results showed that machine learning algorithms can be applied to build the quantitative precipitation estimation model of the polarization radar data in South Korea. The machine learning-based quantitative precipitation estimation models led to better performances than ZR relationship-based models, particularly for heavy rainfall events. The extreme learning machine is considered the best of the algorithms used based on evaluation criteria.

scholarly journals Link quality estimation based on over-sampling and weighted random forest

2021 ◽  
pp. 41-41
Author(s):  
Linlan Liu ◽  
Yi Feng ◽  
Shengrong Gao ◽  
Jian Shu
Keyword(s):  
Random Forest ◽  
Decision Trees ◽  
Estimation Method ◽  
Classification Performance ◽  
Link Quality ◽  
Estimation Methods ◽  
Quality Level ◽  
Estimation Model ◽  
Quality Estimation ◽  
Link Quality Estimation

Aiming at the imbalance problem of wireless link samples, we propose the link quality estimation method which combines the K-means synthetic minority over-sampling technique (K-means SMOTE) and weighted random forest. The method adopts the mean, variance and asymmetry metrics of the physical layer parameters as the link quality parameters. The link quality is measured by link quality level which is determined by the packet receiving rate. K-means is used to cluster link quality samples. SMOTE is employed to synthesize samples for minority link quality samples, so as to make link quality samples of different link quality levels reach balance. Based on the weighted random forest, the link quality estimation model is constructed. In the link quality estimation model, the decision trees with worse classification performance are assigned smaller weight, and the decision trees with better classification performance are assigned bigger weight. The experimental results show that the proposed link quality estimation method has better performance with samples processed by K-means SMOTE. Furthermore, it has better estimation performance than the ones of Naive Bayesian, Logistic Regression and K-nearest Neighbour estimation methods.

scholarly journals Study on the Estimation of Forest Volume Based on Multi-Source Data

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7796
Author(s):  
Tao Hu ◽  
Yuman Sun ◽  
Weiwei Jia ◽  
Dandan Li ◽  
Maosheng Zou ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Hybrid Model ◽  
Prediction Accuracy ◽  
Volume Estimation ◽  
Support Vector ◽  
Estimation Models

We performed a comparative analysis of the prediction accuracy of machine learning methods and ordinary Kriging (OK) hybrid methods for forest volume models based on multi-source remote sensing data combined with ground survey data. Taking Larix olgensis, Pinus koraiensis, and Pinus sylvestris plantations in Mengjiagang forest farms as the research object, based on the Chinese Academy of Forestry LiDAR, charge-coupled device, and hyperspectral (CAF-LiTCHy) integrated system, we extracted the visible vegetation index, texture features, terrain factors, and point cloud feature variables, respectively. Random forest (RF), support vector regression (SVR), and an artificial neural network (ANN) were used to estimate forest volume. In the small-scale space, the estimation of sample plot volume is influenced by the surrounding environment as well as the neighboring observed data. Based on the residuals of these three machine learning models, OK interpolation was applied to construct new hybrid forest volume estimation models called random forest Kriging (RFK), support vector machines for regression Kriging (SVRK), and artificial neural network Kriging (ANNK). The six estimation models of forest volume were tested using the leave-one-out (Loo) cross-validation method. The prediction accuracies of these six models are better, with RLoo2 values above 0.6, and the prediction accuracy values of the hybrid models are all improved to different extents. Among the six models, the RFK hybrid model had the best prediction effect, with an RLoo2 reaching 0.915. Therefore, the machine learning method based on multi-source remote sensing factors is useful for forest volume estimation; in particular, the hybrid model constructed by combining machine learning and the OK method greatly improved the accuracy of forest volume estimation, which, thus, provides a fast and effective method for the remote sensing inversion estimation of forest volume and facilitates the management of forest resources.

A GENETIC ALGORITHM FOR IMPROVING ACCURACY OF SOFTWARE QUALITY PREDICTIVE MODELS: A SEARCH-BASED SOFTWARE ENGINEERING APPROACH

2010 ◽  
Vol 09 (02) ◽  
pp. 125-136 ◽  
Author(s):  
DANIELLE AZAR
Keyword(s):  
Genetic Algorithm ◽  
Software Quality ◽  
Predictive Models ◽  
Learning Algorithm ◽  
Quality Characteristics ◽  
Machine Learning Algorithm ◽  
Quality Estimation ◽  
Improving Accuracy ◽  
Random Guess ◽  
Estimation Models

In this work, we present a genetic algorithm to optimize predictive models used to estimate software quality characteristics. Software quality assessment is crucial in the software development field since it helps reduce cost, time and effort. However, software quality characteristics cannot be directly measured but they can be estimated based on other measurable software attributes (such as coupling, size and complexity). Software quality estimation models establish a relationship between the unmeasurable characteristics and the measurable attributes. However, these models are hard to generalize and reuse on new, unseen software as their accuracy deteriorates significantly. In this paper, we present a genetic algorithm that adapts such models to new data. We give empirical evidence illustrating that our approach out-beats the machine learning algorithm C4.5 and random guess.

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