scholarly journals Comparative Prediction Performance with Support Vector Machine and Random Forest Classification Techniques

2013 ◽  
Vol 69 (11) ◽  
pp. 12-16 ◽  
Author(s):  
Ashfaq AhmedK ◽  
Sultan Aljahdali ◽  
Syed Naimatullah Hussain
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Prediction Performance ◽  
Support Vector ◽  
Classification Techniques ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals Comparison of Classification Methods used in Machine Learning for Dysgraphia Identification

2021 ◽  
Vol 12 (11) ◽  
pp. 1886-1891
Author(s):  
Sarthika Dutt, Et. al.
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Feature Selection Technique ◽  
Random Forest Classification ◽  
Visual Spatial ◽  
Forest Classification

Dysgraphia is a disorder that affects writing skills. Dysgraphia Identification at an early age of a child's development is a difficult task.  It can be identified using problematic skills associated with Dysgraphia difficulty. In this study motor ability, space knowledge, copying skill, Visual Spatial Response are some of the features included for Dysgraphia identification. The features that affect Dysgraphia disability are analyzed using a feature selection technique EN (Elastic Net). The significant features are classified using machine learning techniques. The classification models compared are KNN (K-Nearest Neighbors), Naïve Bayes, Decision tree, Random Forest, SVM (Support Vector Machine) on the Dysgraphia dataset. Results indicate the highest performance of the Random forest classification model for Dysgraphia identification.

scholarly journals Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification

2019 ◽  
Vol 8 (1) ◽  
pp. 39 ◽  
Author(s):  
Zhiqiang Li ◽  
Chengqi Cheng ◽  
Mei-Po Kwan ◽  
Xiaochong Tong ◽  
Shaohong Tian
Keyword(s):  
Random Forest ◽  
Point Cloud ◽  
Asphalt Pavement ◽  
Validation Dataset ◽  
Support Vector ◽  
Cloud Data ◽  
Pavement Distress ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Pavement Distresses

Asphalt pavement ages and incurs various distresses due to natural and human factors. Thus, it is crucial to rapidly and accurately extract different types of pavement distress to effectively monitor road health status. In this study, we explored the feasibility of pavement distress identification using low-altitude unmanned aerial vehicle light detection and ranging (UAV LiDAR) and random forest classification (RFC) for a section of an asphalt road that is located in the suburb of Shihezi City in Xinjiang Province of China. After a spectral and spatial feature analysis of pavement distress, a total of 48 multidimensional and multiscale features were extracted based on the strength of the point cloud elevations and reflection intensities. Subsequently, we extracted the pavement distresses from the multifeature dataset by utilizing the RFC method. The overall accuracy of the distress identification was 92.3%, and the kappa coefficient was 0.902. When compared with the maximum likelihood classification (MLC) and support vector machine (SVM), the RFC had a higher accuracy, which confirms its robustness and applicability to multisample and high-dimensional data classification. Furthermore, the method achieved an overall accuracy of 95.86% with a validation dataset. This result indicates the validity and stability of our method, which highway maintenance agencies can use to evaluate road health conditions and implement maintenance.

scholarly journals The Impact of Simulated Spectral Noise on Random Forest and Oblique Random Forest Classification Performance

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Na’eem Hoosen Agjee ◽  
Onisimo Mutanga ◽  
Kabir Peerbhay ◽  
Riyad Ismail
Keyword(s):  
Random Forest ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Classification ◽  
Node Splitting ◽  
Forest Classification ◽  
Vector Machines ◽  
Classifier Performance ◽  
The Impact

Hyperspectral datasets contain spectral noise, the presence of which adversely affects the classifier performance to generalize accurately. Despite machine learning algorithms being regarded as robust classifiers that generalize well under unfavourable noisy conditions, the extent of this is poorly understood. This study aimed to evaluate the influence of simulated spectral noise (10%, 20%, and 30%) on random forest (RF) and oblique random forest (oRF) classification performance using two node-splitting models (ridge regression (RR) and support vector machines (SVM)) to discriminate healthy and low infested water hyacinth plants. Results from this study showed that RF was slightly influenced by simulated noise with classification accuracies decreasing for week one and week two with the addition of 30% noise. In comparison to RF, oRF-RR and oRF-SVM yielded higher test accuracies (oRF-RR: 5.36%–7.15%; oRF-SVM: 3.58%–5.36%) and test kappa coefficients (oRF-RR: 10.72%–14.29%; oRF-SVM: 7.15%–10.72%). Notably, oRF-RR test accuracies and kappa coefficients remained consistent irrespective of simulated noise level for week one and week two while similar results were achieved for week three using oRF-SVM. Overall, this study has demonstrated that oRF-RR can be regarded a robust classification algorithm that is not influenced by noisy spectral conditions.

scholarly journals Stock Return Prediction using Machine Learning-Based Techniques

2020 ◽  
Vol 18 (12.2) ◽  
pp. 49
Author(s):  
Tra Ngoc Nguyen ◽  
Tien Ho-Phuoc ◽  
Dat Thanh Nguyen ◽  
Minh Nhu Mac
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Stock Return ◽  
Principal Component ◽  
Prediction Performance ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Stock Return Prediction ◽  
And Training

The paper attempts to forecast the intraday return of HNX index by using 3 machine learning models: Support Vector Machine, Random Forest, and Extra-Trees Classifier. Kernel principal component analysis is used for feature extraction and dimension reduction. The prediction performance is compared to the classic Logistic Regression’s. Our empirical results show that Extra-Trees Classifier has the highest prediction accuracy of about 55% which outperforms Logistic Regression by about 0.6%. Although both Extra-Trees Classifier and Random Forest (RF) are based on the same approach, the former always obtains better prediction performance. Besides, while not providing the optimal results, Support Vector Machine seems not to depend on the number of features and training length.

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