Regression and Classification Method Based on Gaussian Processes

2014 ◽  
Vol 971-973 ◽  
pp. 1949-1952
Author(s):  
Xing Hui Wu ◽  
Yu Ping Zhou
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Processes ◽  
Classification Problem ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Regression Problem ◽  
Development Direction

Gaussian processes is a kind of machine learning method developed in recent years and also a promising technology that has been applied both in the regression problem and the classification problem. In this paper, the general principle of regression and classification based on Gaussian process and experimental verification was described. A comparison about accuracy between this method and Support Vector Machine (SVM) is made during the experiments.Finally, it was summarized of the regression and classification of Gaussian process application and future development direction.

A Multi-Classification Method Based on Gaussian Processes

2012 ◽  
Vol 198-199 ◽  
pp. 1333-1337 ◽  
Author(s):  
San Xi Wei ◽  
Zong Hai Sun
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Gaussian Process ◽  
Gaussian Processes ◽  
Good Accuracy ◽  
Classification Problem ◽  
Decision Time ◽  
Support Vector ◽  
Regression Problem ◽  
Multi Classification

Gaussian processes (GPs) is a very promising technology that has been applied both in the regression problem and the classification problem. In recent years, models based on Gaussian process priors have attracted much attention in the machine learning. Binary (or two-class, C=2) classification using Gaussian process is a very well-developed method. In this paper, a Multi-classification (C>2) method is illustrated, which is based on Binary GPs classification. A good accuracy can be obtained through this method. Meanwhile, a comparison about decision time and accuracy between this method and Support Vector Machine (SVM) is made during the experiments.

scholarly journals Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

2020 ◽  
Vol 44 (6) ◽  
pp. 415-427
Author(s):  
Jung Ho Yang ◽  
Jae Hyeon Park ◽  
Seong-Ho Jang ◽  
Jaesung Cho
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Knee Osteoarthritis ◽  
Logistic Regression Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Machine Learning Method ◽  
Learning Method

Objective To present new classification methods of knee osteoarthritis (KOA) using machine learning and compare its performance with conventional statistical methods as classification techniques using machine learning have recently been developed.Methods A total of 84 KOA patients and 97 normal participants were recruited. KOA patients were clustered into three groups according to the Kellgren-Lawrence (K-L) grading system. All subjects completed gait trials under the same experimental conditions. Machine learning-based classification using the support vector machine (SVM) classifier was performed to classify KOA patients and the severity of KOA. Logistic regression analysis was also performed to compare the results in classifying KOA patients with machine learning method.Results In the classification between KOA patients and normal subjects, the accuracy of classification was higher in machine learning method than in logistic regression analysis. In the classification of KOA severity, accuracy was enhanced through the feature selection process in the machine learning method. The most significant gait feature for classification was flexion and extension of the knee in the swing phase in the machine learning method.Conclusion The machine learning method is thought to be a new approach to complement conventional logistic regression analysis in the classification of KOA patients. It can be clinically used for diagnosis and gait correction of KOA patients.

Using Machine Learning Method for classification Body Mass Index for clinical decision

2021 ◽  
Author(s):  
Firouz Amani ◽  
Alireza Mohammadnia ◽  
Paniz Amani
Keyword(s):  
Machine Learning ◽  
Body Mass Index ◽  
At Risk ◽  
Body Mass ◽  
Overweight And Obesity ◽  
Support Vector ◽  
Classification Algorithms ◽  
Machine Learning Method ◽  
Learning Method

Abstract Background: Body mass index (BMI) is a good method for measure the overweight and obesity among people. The aim of this study was to develop a machine learning method to classification of BMI for clinical application.Methods: In this study we used the dataset of 1316 people who selected randomly from all area of Ardabil city. Dataset included demographic and anthropometric data. Classification algorithms such as Random forest (RF), Gaussian Naïve Bayes (GNB), Decision Tree (DT), Support-Vector Machines (SVM), Multi-layer Perceptron (MLP), K-nearest neighbors (KNN) and Logistic Regression (LR) were used for classification of people based on BMI data. The performance of algorithms were evaluated with Precision, Recall, Mean Squared Errors (MSE) and Accuracy. All programing done in python.3.7 in Jupyter Notebook. Results: According to BMI, 603(45.8%) of all samples were normal and 713 (54.2%) were at-risk. The precision of RF, GNB, DT, SVM, MLP, KNN and LR for people at risk was 0.93, 0.86, 0.99, 0.82, 100, 0.82 and 0.99 respectively. Also, the accuracy of RF, GNB, DT, SVM, MLP, KNN and LR were 95%, 83%, 100%, 82%, 100%, 82% and 100 %. Conclusion: In compare classification algorithms results showed that, the LR , MLP and DT had the higher full accuracy than other algorithms in detection of people at-risk.

scholarly journals Analysis of IPL Match Results using Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 1746-1751
Author(s):  
N. Lokeswari
Keyword(s):  
Machine Learning ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Large Set ◽  
Regression Problem ◽  
The Past ◽  
Premier League ◽  
Past Data

Indian Premier League (IPL) is a famous Twenty-20 League conducted by The Board of Control for Cricket in India (BCCI). It was started in 2008 and successfully completed its thirteen seasons till 2020. IPL is a popular sport where it has a large set of audience throughout the country. Every cricket fan would be eager to know and predict the IPL match results.A solution using Machine Learning is provided for the analysis of IPL Match results. This paper attempts to predict the match winner and the innings score considering the past data of match by match and ball by ball. Match winner prediction is taken as classification problem and innings score prediction is taken as regression problem. Algorithms like Support Vector Machine(SVM),Naive Bayes, k-Nearest Neighbour(kNN) are used for classification of match winner and Linear Regression, Decision tree for prediction of innings score. The dataset contains many features in which 7 features are identified in which that can be used for the prediction. Based on those features, models are built and evaluated by certain parameters. Based on the results SVM performed.

scholarly journals Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method

2021 ◽  
Vol 13 (1) ◽  
pp. 133
Author(s):  
Hao Sun ◽  
Yajing Cui
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Land Surface ◽  
Regional Scale ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Feed Forward Neural Network ◽  
Comparative Performance ◽  
Geographical Factors

Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors; (2) adding geographical factors can significantly improve the performance of SM downscaling; (3) integrating LST, LEE, and geographical factors got the best performance; (4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.

scholarly journals Methodology Proposal of ADHD Classification of Children Based on Cross Recurrence Plots

2021 ◽  
Author(s):  
Marco Aceves-Fernandez
Keyword(s):  
Machine Learning ◽  
Spectral Distribution ◽  
High Dimensional ◽  
Control Group ◽  
Machine Learning Method ◽  
Learning Method ◽  
Recurrence Plots ◽  
Eeg Signals ◽  
Power Spectral

Abstract Dealing with electroencephalogram signals (EEG) are often not easy. The lack of predicability and complexity of such non-stationary, noisy and high dimensional signals is challenging. Cross Recurrence Plots (CRP) have been used extensively to deal with detecting subtle changes in signals even when the noise is embedded in the signal. In this contribution, a total of 121 children performed visual attention experiments and a proposed methodology using CRP and a Welch Power Spectral Distribution have been used to classify then between those who have ADHD and the control group. Additional tools were presented to determine to which extent the proposed methodology is able to classify accurately and avoid misclassifications, thus demonstrating that this methodology is feasible to classify EEG signals from subjects with ADHD. Lastly, the results were compared with a baseline machine learning method to prove experimentally that this methodology is consistent and the results repeatable.

Research on enterprise financial economics early warning based on machine learning method

2021 ◽  
pp. 1-11
Author(s):  
Jian Yi
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Financial Economics ◽  
Rapid Development ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Combined Model ◽  
Svm Algorithm ◽  
The Stability

The stability of the economic market is an important factor for the rapid development of the economy, especially for the listed companies, whose financial and economic stability affects the stability of the financial market. It is helpful for the healthy development of enterprises and financial markets to make an accurate early warning of the financial economy of listed enterprises. This paper briefly introduced the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms in the machine learning method. To make up for the defects of the two algorithms, they were combined and applied to the enterprise financial economics early warning. A simulation experiment was carried out on the single SVM algorithm-based, single BPNN algorithm-based, and SVM algorithm and BPNN algorithm combined model with the MATLAB software. The results show that the SVM algorithm and BP algorithm combined model converges faster and has higher precision and recall rate and larger area under the curve (AUC) than the single SVM algorithm-based model and the single BPNN algorithm-based model.

scholarly journals Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China

2020 ◽  
Vol 10 (11) ◽  
pp. 4016 ◽  
Author(s):  
Xudong Hu ◽  
Han Zhang ◽  
Hongbo Mei ◽  
Dunhui Xiao ◽  
Yuanyuan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Southwest China ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Statistical Measures ◽  
Ensemble Machine Learning

Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention and mitigation. However, delineating the spatial occurrence pattern of the landslide remains a challenge. This study investigates the potential application of the stacking ensemble learning technique for landslide susceptibility assessment. In particular, support vector machine (SVM), artificial neural network (ANN), logical regression (LR), and naive Bayes (NB) were selected as base learners for the stacking ensemble method. The resampling scheme and Pearson’s correlation analysis were jointly used to evaluate the importance level of these base learners. A total of 388 landslides and 12 conditioning factors in the Lushui area (Southwest China) were used as the dataset to develop landslide modeling. The landslides were randomly separated into two parts, with 70% used for model training and 30% used for model validation. The models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and statistical measures. The results showed that the stacking-based ensemble model achieved an improved predictive accuracy as compared to the single algorithms, while the SVM-ANN-NB-LR (SANL) model, the SVM-ANN-NB (SAN) model, and the ANN-NB-LR (ANL) models performed equally well, with AUC values of 0.931, 0.940, and 0.932, respectively, for validation stage. The correlation coefficient between the LR and SVM was the highest for all resampling rounds, with a value of 0.72 on average. This connotes that LR and SVM played an almost equal role when the ensemble of SANL was applied for landslide susceptibility analysis. Therefore, it is feasible to use the SAN model or the ANL model for the study area. The finding from this study suggests that the stacking ensemble machine learning method is promising for landslide susceptibility mapping in the Lushui area and is capable of targeting areas prone to landslides.

Sign in / Sign up

Export Citation Format

Share Document