Classification Accuracy Of Random Forest Research Articles

Evaluation of deep learning Convolutional Neural Network for Crop Classification

In this paper, we have done exploratory experiments using deep learning convolutional neural network framework to classify crops into cotton, sugarcane and mulberry. In this contribution we have used Earth Observing-1 hyperion hyperspectral remote sensing data as the input. Structured data has been extracted from hyperspectral data using a remote sensing tool. An analytical assessment shows that convolutional neural network (CNN) gives more accuracy over classical support vector machine (SVM) and random forest methods. It has been observed that accuracy of SVM is 75 %, accuracy of random forest classification is 78 % and accuracy of CNN using Adam optimizer is 99.3 % and loss is 2.74 %. CNN using RMSProp also gives the same accuracy 99.3 % and the loss is 4.43 %. This identified crop information will be used for finding crop production and for deciding market strategies.

DPRF: A Differential Privacy Protection Random Forest

Providing privacy protection for classification algorithms has become a research hotspot in current data mining. In this paper, differential privacy is applied to the random forest classification algorithm, and a random forest algorithm based on differential privacy is proposed to protect the privacy information in the data classification process. Firstly, differential privacy provides privacy protection by adding perturbation noise, which leads to a decrease in the classification accuracy of random forest algorithms. In order to reduce the impact of differential privacy protection on the accuracy of random forest classification, a hybrid decision tree algorithm is proposed. For the construction of a single decision tree in a random forest, the information gain ratio in the ID3 algorithm and the information gain ratio in the C4.5 are combined to generate a new attribute metric IG_GR to improve the classification accuracy of a single decision tree. Secondly, a new privacy budget allocation strategy is proposed. For nodes of different depths in the decision tree, the privacy budget is allocated to its counting query and attribute query by weight, which is used to balance the signal-to-noise ratio of differential privacy technology to nodes of different depths in the decision tree. At the same time, the hybrid decision tree algorithm is applied to the construction of random forest, which balances the privacy and classification accuracy of the random forest algorithm based on differential privacy. Finally, this paper conducted experiments on UCI's Adult and Mushroom datasets. The results show that compared with the traditional decision tree algorithm, the algorithm proposed in this paper has better classification accuracy; and the DPRF can provide effective privacy protection under the premise of ensuring high classification performance. The work of this paper achieves a balance between privacy and classification accuracy, and has practical application value.

Correlation between Gait Asymmetry and Leg Length Discrepancy—What Is the Role of Clinical Abnormalities?

Reducing the effect of leg length discrepancy (LLD) on gait abnormalities while other abnormal conditions such as spasticity, joint contractures or weak muscle strength are exhibited is challenging. This study aimed to evaluate the impact of mild LLD on lower limb biomechanics, on participants with anatomic LLD with and without other clinical abnormalities. A motion capture system was utilized on 32 participants to measure lower limb kinematics and dynamic leg length (DLL) throughout the gait cycle, calculated as the absolute distance from the hip joint center, either to the heel, ankle joint center, or forefoot. The Pearson correlation coefficient found that LLD was associated with 5 kinematic variables only when LLD appeared with no other clinical abnormalities present (r = 0.574 – 0.846, p < 0.05). When clinical abnormalities were present, the random forest classification accuracy was lower (64% versus 80%), implying that the used kinematics are low predictors for anatomic LLD, revealing a higher asymmetrical clearance index (the difference between the maximal stance phase and the minimal contra-lateral swing phase DLL) and a different kinematic variable importance index. Clinical abnormalities in pathological gait will in all probability significantly affect gait deviations, affirming mild anatomic LLD as inconsequential. A functional measurement can offer a better estimate as to the side and extent of the functional discrepancy.

Characterization and classification of volatiles from different breeds of eggs by SPME-GC–MS and chemometrics

Characterization and classification of volatiles from different breeds of eggs by SPME-GC–MS and chemometrics

Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region

The objective of this research was to investigate the impact of seasonality on urban land-cover mapping and to explore better classification accuracy by using multi-season Sentinel-1A and GF-1 wide field view (WFV) images, and the combinations of both types of images in subtropical monsoon-climate regions in Southeast China. We obtained multi-season Sentinel-1A and GF-1 WFV images, as well as the combinations of both data, by using a support vector machine (SVM) and a random forest (RF) classifier. The backscatter intensity, texture, and interference-coherence images were extracted from Sentinel-1A images, and different combinations of these Sentinel-1A-derived images were used to evaluate their ability to map urban land cover. The results showed that the performance of winter images was better than that of any other season, while the summer images performed the worst. Higher classification accuracy was achieved by using multi-season images, and satisfactory classification results were obtained when using Sentinel-1A images from only three seasons. The best classification result was achieved using a combination of all Sentinel-1A data from all four seasons and GF-1 WFV data from winter, with an overall accuracy of up to 96.02% and a kappa coefficient reaching 0.9502. The performance of textures was slightly better than that of the backscatter-intensity images. Although the coherence data performed the worst, it was still able to distinguish urban impervious surfaces well. In addition, the overall classification accuracy of RF was better than that of SVM.

Factors Associated with HIV Testing Among Participants from Substance Use Disorder Treatment Programs in the US: A Machine Learning Approach.

HIV testing is the foundation for consolidated HIV treatment and prevention. In this study, we aim to discover the most relevant variables for predicting HIV testing uptake among substance users in substance use disorder treatment programs by applying random forest (RF), a robust multivariate statistical learning method. We also provide a descriptive introduction to this method for those who are unfamiliar with it. We used data from the National Institute on Drug Abuse Clinical Trials Network HIV testing and counseling study (CTN-0032). A total of 1281 HIV-negative or status unknown participants from 12 US community-based substance use disorder treatment programs were included and were randomized into three HIV testing and counseling treatment groups. The a priori primary outcome was self-reported receipt of HIV test results. Classification accuracy of RF was compared to logistic regression, a standard statistical approach for binary outcomes. Variable importance measures for the RF model were used to select the most relevant variables. RF based models produced much higher classification accuracy than those based on logistic regression. Treatment group is the most important predictor among all covariates, with a variable importance index of 12.9%. RF variable importance revealed that several types of condomless sex behaviors, condom use self-efficacy and attitudes towards condom use, and level of depression are the most important predictors of receipt of HIV testing results. There is a non-linear negative relationship between count of condomless sex acts and the receipt of HIV testing. In conclusion, RF seems promising in discovering important factors related to HIV testing uptake among large numbers of predictors and should be encouraged in future HIV prevention and treatment research and intervention program evaluations.

Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings

Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings

Random Forest Algorithm for Land Cover Classification

Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial neural, decision trees, support vector machines, and ensembles classifiers. Various ensemble classification algorithms have been proposed in recent years. Most widely used ensemble classification algorithm is Random Forest. The Random Forest classifier uses bootstrap aggregating for form an ensemble of classification and induction tree like tree classifiers. A few researchers have used Random Forest for land cover analysis. However, the potential of Random Forest has not yet been fully explored by the remote sensing community. In this paper we compare classification accuracy of Random Forest with other commonly used algorithms such as the maximum likelihood, minimum distance, decision tree, neural network, and support vector machine classifiers.

The Effects of Point or Polygon Based Training Data on RandomForest Classification Accuracy of Wetlands

Wetlands are dynamic in space and time, providing varying ecosystem services. Field reference data for both training and assessment of wetland inventories in the State of Minnesota are typically collected as GPS points over wide geographical areas and at infrequent intervals. This status-quo makes it difficult to keep updated maps of wetlands with adequate accuracy, efficiency, and consistency to monitor change. Furthermore, point reference data may not be representative of the prevailing land cover type for an area, due to point location or heterogeneity within the ecosystem of interest. In this research, we present techniques for training a land cover classification for two study sites in different ecoregions by implementing the RandomForest classifier in three ways: (1) field and photo interpreted points; (2) fixed window surrounding the points; and (3) image objects that intersect the points. Additional assessments are made to identify the key input variables. We conclude that the image object area training method is the most accurate and the most important variables include: compound topographic index, summer season green and blue bands, and grid statistics from LiDAR point cloud data, especially those that relate to the height of the return.

Statistical analysis of cross-correlation sample of 3XMM-DR4 with SDSS-DR10 and UKIDSS-DR9

AbstractWe match the XMM-Newton 3XMMi-DR4 catalog with the Sloan Digital Sky Survey (SDSS) Data Release 10 and the United Kingdom Infrared Deep Sky Survey (UKIDSS) Data Release 9. Based on this X-ray/optical/infrared catalog, we probe the distribution of various types of X-ray emitters in the multidimensional parameter space. It is found that quasars, galaxies and stars have some kind distribution rule, especially for stars. The result shows that only using the X-ray/optical features, stars are difficult to discriminate from galaxies and quasars, the added information from infrared band is very helpful to improve the classification result of any classifier. Comparing the classification accuracy of random forests with that of rotation forests, rotation forests show better performance.

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

Predicting changes in protein thermostability brought about by single- or multi-site mutations

BackgroundAn important aspect of protein design is the ability to predict changes in protein thermostability arising from single- or multi-site mutations. Protein thermostability is reflected in the change in free energy (ΔΔG) of thermal denaturation.ResultsWe have developed predictive software, Prethermut, based on machine learning methods, to predict the effect of single- or multi-site mutations on protein thermostability. The input vector of Prethermut is based on known structural changes and empirical measurements of changes in potential energy due to protein mutations. Using a 10-fold cross validation test on the M-dataset, consisting of 3366 mutants proteins from ProTherm, the classification accuracy of random forests and the regression accuracy of random forest regression were slightly better than support vector machines and support vector regression, whereas the overall accuracy of classification and the Pearson correlation coefficient of regression were 79.2% and 0.72, respectively. Prethermut performs better on proteins containing multi-site mutations than those with single mutations.ConclusionsThe performance of Prethermut indicates that it is a useful tool for predicting changes in protein thermostability brought about by single- or multi-site mutations and will be valuable in the rational design of proteins.