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نتیجه جستجو - Random forests

تعداد مقالات یافته شده: 28
ردیف عنوان نوع
1 Non-destructive and contactless estimation of chlorophyll and ammonia contents in packaged fresh-cut rocket leaves by a Computer Vision System
تخمین غیر مخرب و بدون تماس محتویات کلروفیل و آمونیاک در برگ های موشک تازه برش خورده بسته بندی شده توسط یک سیستم کامپیوتر ویژن-2022
Computer Vision Systems (CVS) offer a non-destructive and contactless tool to assign visual quality level to fruit and vegetables and to estimate some of their internal characteristics. The innovative CVS described in this paper exploits the combination of image processing techniques and machine learning models (Random Forests) to assess the visual quality and predict the internal traits on unpackaged and packaged rocket leaves. Its perfor- mance did not depend on the cultivation system (traditional soil or soilless). The same CVS, exploiting its ma- chine learning components, was able to build effective models for either the classification problem (visual quality level assignment) and the regression problems (estimation of senescence indicators such as chlorophyll and ammonia contents) just by changing the training data. The experiments showed a negligible performance loss on packaged products (Pearson’s linear correlation coefficient of 0.84 for chlorophyll and 0.91 for ammonia) with respect to unpackaged ones (0.86 for chlorophyll and 0.92 for ammonia). Thus, the non-destructive and con- tactless CVS represents a valid alternative to destructive, expensive and time-consuming analyses in the lab and can be effectively and extensively used along the whole supply chain, even on packaged products that cannot be analyzed using traditional tools.
keywords: Contactless quality level assessment | Diplotaxis tenuifolia L | Image analysis | Packaged vegetables | Senescence indicators prediction
مقاله انگلیسی
2 Data-driven switching modeling for MPC using Regression Trees and Random Forests
مدل سازی سوئیچینگ داده محور برای MPC با استفاده از درختان رگرسیون و جنگل های تصادفی-2020
Model Predictive Control is a well consolidated technique to design optimal control strategies, leveraging the capability of a mathematical model to predict a system’s behavior over a time horizon. However, building physics-based models for complex large-scale systems can be cost and time prohibitive. To overcome this problem we propose a methodology to exploit machine learning techniques (i.e. Regression Trees and Random Forests) in order to build a Switching Affine dynamical model (deterministic and Markovian) of a large-scale system using historical data, and apply Model Predictive Control. A comparison with an optimal benchmark and related techniques is provided on an energy management system to validate the performance of the proposed methodology.
Keywords: Regression Trees | Random Forests | Model predictive control | Switching systems | Markov Jump Systems
مقاله انگلیسی
3 Comparing performance of ensemble methods in predicting movie box office revenue
مقایسه عملکرد روش های گروه در پیش بینی درآمد گیشه فیلم-2020
While many business intelligence methods have been applied to predict movie box office revenue, the studies using an ensemble approach to predict box office revenue are almost nonexistent. In this study, we propose decision trees, k-nearest-neighbors (k-NN), and linear regression using ensemble methods and the prediction performance of decision trees based on random forests, bagging and boosting are compared with that of k-NN and linear regression based on bagging and boosting using the sample of 1439 movies. The results indicate that ensemble methods based on decision trees (random forests, bagging, boosting) outperform ensemble methods based on k-NN (bagging, boosting) in predicting box office at week 1, 2, 3 after release. Decision trees using ensemble methods provide better prediction performance than ensemble methods based on linear regression analysis in the box office at week 1 after release. This is explained by the results that after comparing the pre- diction performance between ensemble methods and non-ensemble methods. For decision tree methods, unlike the other methods, the prediction performance of ensemble methods is greater than that of non-ensemble methods. This shows that decision trees using ensemble methods provide better application effectiveness of ensemble methods than k-NN and linear regression analysis.
Keywords: Movie box office revenue | Ensemble methods | Prediction of box office revenue | Decision trees | Data analysis | Data analytics | Big data | Management | Business management
مقاله انگلیسی
4 Automatic bad channel detection in implantable brain-computer interfaces using multimodal features based on local field potentials and spike signals
تشخیص خودکار کانال بد در رابط های قابل کاشت مغز با کامپیوتر با استفاده از ویژگی های چند حالته بر اساس پتانسیل های محلی و سیگنال های لبه-2020
“Bad channels” in implantable multi-channel recordings bring troubles into the precise quantitative description and analysis of neural signals, especially in the current “big data” era. In this paper, we combine multimodal features based on local field potentials (LFPs) and spike signals to detect bad channels automatically using machine learning. On the basis of 2632 pairs of LFPs and spike recordings acquired from five pigeons, 12 multimodal features are used to quantify each channel’s temporal, frequency, phase and firing-rate properties. We implement seven classifiers in the detection tasks, in which the synthetic minority oversampling technique (SMOTE) system and Fisher weighted Euclidean distance sorting (FWEDS) are used to cope with the class imbalance problem. The results of the two-dimensional scatterplots and classifications demonstrate that correlation coefficient, phase locking value, and coherence have good discriminability. For the multimodal features, almost all the classifiers can obtain high accuracy and bad channel detection rate after the SMOTE operation, in which the Random Forests classifier shows relatively better comprehensive performance (accuracy: 0.9092 � 0.0081, precision: 0.9123 � 0.0100, and recall: 0.9057 � 0.0121). The proposed approach can automatically detect bad channels based on multimodal features, and the results provide valuable references for larger datasets.
Keywords: Bad channel | Multimodal feature | LFP | Spike | Machine learning
مقاله انگلیسی
5 Estimating monthly wet sulfur (S) deposition flux over China using an ensemble model of improved machine learning and geostatistical approach
برآورد شار رسوب ماهانه گوگرد مرطوب (S) بر روی چین با استفاده از مدل گروهی از یادگیری ماشین پیشرفته و روش زمین آماری-2019
The wet S deposition was treated as a key issue because it played the negative on the soil acidification, biodiversity loss, and global climate change. However, the limited ground-level monitoring sites make it difficult to fully clarify the spatiotemporal variations of wet S deposition over China. Therefore, an ensemble model of improved machine learning and geostatistical method named fruit fly optimization algorithm-random forestspatiotemporal Kriging (FOA-RF-STK) model was developed to estimate the nationwide S deposition based on the emission inventory, meteorological factors, and other geographical covariates. The ensemble model can capture the relationship between predictors and S deposition flux with the better performance (R2=0.68, root mean square error (RMSE)=7.51 kg ha−1 yr−1) compared with the original RF model (R2=0.52, RMSE=8.99 kg ha−1 yr−1). Based on the improved model, it predicted that the highest and lowest S deposition flux were mainly concentrated on the Southeast China (69.57 kg S ha−1 yr−1) and Inner Mongolia (42.37 kg S ha−1 yr−1), respectively. The estimated wet S deposition flux displayed the remarkably seasonal variation with the highest value in summer (22.22 kg S ha−1 sea−1), follwed by ones in autumn (18.30 kg S ha−1 sea−1), spring (16.27 kg S ha−1 sea−1), and the lowest one in winter (14.71 kg S ha−1 sea−1), which was closely associated with the rainfall amounts. The study provides a novel approach for the S deposition estimation at a national scale.
Keywords: Wet S deposition | Machine learning | Geostatistical approach | China
مقاله انگلیسی
6 Data mining approach based on chemical composition of grape skin for quality evaluation and traceability prediction of grapes
رویکرد داده کاوی مبتنی بر ترکیب شیمیایی پوست انگور برای ارزیابی کیفیت و پیش بینی قابلیت ردیابی انگور-2019
The knowledge of wine origin is an important aspect in winemaking industries due to the Denomination of Controlled Origin. In this work, a data mining algorithms comparison study of grape-skin samples from five regions of Mendoza, Argentina, and builds classification models capable of predicting provenance based on multi-elemental composition, were developed. Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine 29 elements (Ag, As, Ce, Co, Cs, Cu, Eu, Fe, Ga, Gd, La, Lu, Mn, Mo, Nb, Nd, Ni, Pr, Rb, Sm, Te, Ti, Tl, Tm, U, V, Y, Zn and Zr). Four classification techniques, including multinomial logistic regression (MLR), knearest neighbors (k-NN), support vector machines (SVM), and random forests (RF) were assessed. The best results were achieved for SVM and RF models, with 84% and 88.9% prediction accuracy, respectively, on the 10- fold cross validation. The RF variable importance showed that Rb (rubidium) was the most relevant components for prediction.
Keywords: Machine learning | Grape-skins | Mineral content | Provenance
مقاله انگلیسی
7 Using gait analysis’ parameters to classify Parkinsonism: A data mining approach
استفاده از پارامترهای تحلیل راه رفتن برای طبقه بندی پارکینسونیسم: یک رویکرد داده کاوی-2019
Introduction: Parkinson’s disease (PD) is the second most common neurodegenerative disorder in the world, while Progressive Supranuclear Palsy (PSP) is an atypical Parkinsonism resembling PD, especially in early stage. Assumed that gait dysfunctions represent a major motor symptom for both pathologies, gait analysis can provide clinicians with subclinical information reflecting subtle differences between these diseases. In this scenario, data mining can be exploited in order to differentiate PD patients at different stages of the disease course and PSP using all the variables acquired through gait analysis. Methods: A cohort of 46 subjects (divided into three groups) affected by PD patients at different stages and PSP patients was acquired through gait analysis and spatial and temporal parameters were anal- ysed. Synthetic Minority Over-sampling Technique was used to balance our imbalanced dataset and cross- validation was applied to provide different training and testing sets. Then, Random Forests and Gradient Boosted Trees were implemented. Results: Accuracy, error, precision, recall, specificity and sensitivity were computed for each group and for both algorithms, including 16 features. Random Forests obtained the highest accuracy (86.4%) but also specificity and sensitivity were particularly high, overcoming the 90% for PSP group. Conclusion: The novelty of the study is the use of a data mining approach on the spatial and temporal parameters of gait analysis in order to classify patients affected by typical (PD) and atypical Parkinsonism (PSP) based on gait patterns. This application would be helpful for clinicians to distinguish PSP from PD at early stage, when the differential diagnosis is particularly challenging.
Keywords: Parkinson’s disease |Progressive supranuclear palsy | Gait analysis | Data mining | Random forests | Gradient boosted trees
مقاله انگلیسی
8 Dropout early warning systems for high school students using machine learning
ترک سیستم های هشدار اولیه برای دانش آموزان دبیرستانی که از یادگیری ماشین استفاده می کنند-2019
Students dropouts are a serious problem for students, society, and policy makers. Predictive modeling using machine learning has a great potential in developing early warning systems to identify students at risk of dropping out in advance and help them. In this study, we use the random forests in machine learning to predict students at risk of dropping out. The data used in this study are the samples of 165,715 high school students from the 2014 National Education Information System (NEIS), which is a national system for educational administration information connected through the Internet with around 12,000 elementary and secondary schools, 17 city/provincial offices of education, and the Ministry of Education in Korea. Our predictive model showed an excellent performance in predicting students dropouts in terms of various performance metrics for binary classification. The results of our study demonstrate the benefit of using machine learning with students big data in education. We briefly overview machine learning in general and the random forests model and present the various performance metrics to evaluate our predictive model.
Keywords: Dropout | Machine learning | Predictive model | Random forests model | Big data
مقاله انگلیسی
9 Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation
ارزیابی یادگیری ماشین مبتنی بر دما و مدلهای تجربی برای پیش بینی تابش روزانه خورشیدی جهانی-2019
Accurate global solar radiation data are fundamental information for the allocation and design of solar energy systems. The current study compared different machine learning and empirical models for global solar radiation prediction only using air temperature as inputs. Four machine learning models, e.g., hybrid mind evolutionary algorithm and artificial neural network model, original artificial neural network, random forests and wavelet neural network, as well as four empirical temperature-based models (Hargreaves-Samani model, Bristow- Campbell model, Jahani model, and Fan model) were applied for prediction of daily global solar radiation in temperate continental regions of China. The results indicated the hybrid mind evolutionary algorithm and artificial neural network model provided better estimations, compared with the existing machine learning and empirical models. Thus, the temperature-based hybrid model is highly recommended to predict global solar radiation in temperate continental regions of China when only air temperature data are available. Combining the hybrid model with future air temperature forecasts, we can get the accurate information of future solar radiation, which is of great importance to management and operation of solar energy systems.
Keywords: Global solar radiation | Forecast | Empirical models | Machine learning models | Temperate continental regions
مقاله انگلیسی
10 Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction
اهمیت انتخاب متغیر پیش بینی کننده مکانی در برنامه های یادگیری ماشین - انتقال از تولید مثل داده ها به پیش بینی مکانی-2019
Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is problematic and results in models that can reproduce training data but are unable to make spatial predictions beyond the locations of the training samples. We assume that not only spatial validation strategies but also spatial variable selection is essential for reliable spatial predictions. We introduce two case studies that use remote sensing to predict land cover and the leaf area index for the “Marburg Open Forest”, an open research and education site of Marburg University, Germany. We use the machine learning algorithm Random Forests to train models using non-spatial and spatial cross-validation strategies to understand how spatial variable selection affects the predictions. Our findings confirm that spatial cross-validation is essential in preventing overoptimistic model performance. We further show that highly autocorrelated predictors (such as geolocation variables, e.g. latitude, longitude) can lead to considerable overfitting and result in models that can reproduce the training data but fail in making spatial predictions. The problem becomes apparent in the visual assessment of the spatial predictions that show clear artefacts that can be traced back to a misinterpretation of the spatially autocorrelated predictors by the algorithm. Spatial variable selection could automatically detect and remove such variables that lead to overfitting, resulting in reliable spatial prediction patterns and improved statistical spatial model performance. We conclude that in addition to spatial validation, a spatial variable selection must be considered in spatial prediction models of ecological data to produce reliable results.
Keywords: Cross-validation | Environmental monitoring | Machine learning | Overfitting | Random Forests | Remote sensing
مقاله انگلیسی
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