Weighted Random Forest Algorithm Based on Bayesian Algorithm

In this paper, a semi-supervised random forest (RF) algorithm is presented for fault diagnosis of rotating machinery. Firstly, many unlabeled samples are divided into two parts, denoted respectively as unlabeled sample I and unlabeled sample II. Then a graph-all the labeled samples are used to train the multiple decision trees. If the classification result is consistent with the one of label prediction, then the unlabeled sample I is added to the labeled samples and used for building RF model. While, the data of unlabeled sample II are utilized for testing of the obtained RF model. Finally, the developed RF algorithm is applied to an experimental platform of rotating machinery. It is shown from the simulation results that, for the cases of noisy samples with unsatisfying labels, the new developed semi-supervised RF algorithm can improve the fault classification accuracy than the conventional RF.

Skin disease is a very vulnerable and severe issue in today's world. Skin disorder classification is crucial for diagnosis. Several new data mining algorithms have been developed to classify and interpret medical images. The functionality of the K-Nearest Neighbors (KNN) Random Forest (RF) Algorithm is described in this article, along with an analysis of its results. Furthermore, this study demonstrates a high-performing approach that saves both effort and money. The proposed model is designed based on KNN and the Random Forest algorithm. The patient can use this model to classify his skin disease as a primary detection, and the doctor also can ensure his judgment by using this proposed model. Traditional skin disease diagnosis is an expensive and time-consuming procedure. This paper's proposed classification model will identify ten different skin diseases. The Random Forest algorithm has a testing accuracy of 94.22 percent, and K-Nearest Neighbors (KNN) has a testing accuracy of 95.23 percent. The KNN algorithm has an F1 Score of 95.98 percent, whereas the Random Forest (RF) algorithm has an F1 Score of 95.94 percent. It can be increased by expanding the dataset and more feature extraction. This approach may benefit individuals with skin illness who are looking to save money and time as well as avoid skin cancer by identifying cancer at an early stage.

The Random Forests (RF) algorithm is a machine learning method that has recently been demonstrated as a viable technique for data-driven predictive modeling of mineral prospectivity, and thus, it is instructive to further examine its usefulness in this particular field. A case study was carried out using data from Catanduanes Island (Philippines) to investigate further (a) if RF modeling can be used for data-driven modeling of mineral prospectivity in areas with few (i.e., <20) mineral occurrences and (b) if RF modeling can handle predictor variables with missing values. We found that RF modeling outperforms evidential belief (EB) modeling of prospectivity for hydrothermal Au–Cu deposits in Catanduanes Island, where 17 hydrothermal Au–Cu prospects are known to exist. Moreover, just like EB modeling, RF modeling allows analysis of the spatial relationships between known prospects and individual layers of predictor data. Furthermore, RF modeling can handle missing values in predictor data through an RF-based imputation technique whereas in EB modeling, missing values are simply represented by maximum uncertainty. Therefore, the RF algorithm is a potentially useful method for data-driven predictive modeling of mineral prospectivity in regions with few (i.e., <20) occurrences of mineral deposits of the type sought. However, further testing of the method in other regions with few mineral occurrences is warranted to fully determine its usefulness in data-driven predictive modeling of mineral prospectivity.

Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines)

The advancing information technology is playing more and more important role in data mining of relational database.1 The transfer and sharing of databases cause the copyright-related security threats. Database watermarking technology can effectively solve the problem with copyright protection and traceability, which has been attracting researchers' attention. In this paper, we proposed a novel, robust and reversible database watermarking technique, named histogram shifting watermarking based on random forest and genetic algorithm (RF-GAHCSW). It greatly improves the watermark capacity by means of histogram width reduction and eliminates the impact of the prediction error attack. Meanwhile, random forest algorithm is used to select important attributes for watermark embedding, and genetic algorithm is employed to find the optimal secret key for the database grouping and determine the position of watermark embedding to improve the watermark capacity and reduce data distortion. The experimental results show that the robustness of RF-GAHCSW is greatly improved, compared with the original HSW, and the distortion has little effect on the usability of database.

Lameness is a serious disease that affects the health and welfare of dairy cattle whilst also causing yield and economic losses. The primary goal of this study is to determine if lameness can be detected early on in herd management using the Random Forest (RF) algorithm and the surface temperatures of the cows' hoof soles, as well as the digital colour parameters generated by processing these thermal camera images. Ages, hoof sole temperatures, and digital colour characteristics of 40 Simmental cattle were used as independent variables in this study, while lameness was evaluated by scoring and employed as a dependent variable after being updated as a binary variable. The parameters ntree= 100 and mtry= 3 were used to develop the RF algorithm for predicting lameness in animals. As a result, the RF algorithm correctly classified 19 of 22 healthy animals and incorrectly classified 3, while it correctly classified 15 of 18 unhealthy animals and incorrectly classified 3. The classification success of the RF algorithm was 85%, sensitivity, specificity and area under the ROC curve (AUC) were 0.864, 0.833, and 0.848±0.059, respectively, and it was successful in detecting lameness. Also, AUC, which is one of the RF algorithm's classification performances, was found to be statistically significant (P&amp;lt;0.05). As a direct consequence it can be stated that the RF algorithm is a suitable classifier in terms of the use of animal hoof sole temperatures and digital colour parameters obtained through image processing in the detection of lameness in herd management.

Application of random forest algorithm in hail forecasting over Shandong Peninsula

Accurate estimation of dew point temperature (T_dew) plays a very important role in the fields of water resource management, agricultural engineering, climatology and energy utilization. However, there are few studies on the applicability of local T_dew algorithms at regional scales. This study evaluated the performance of a new machine learning algorithm, i.e., gradient boosting on decision trees with categorical features support (CatBoost) to estimate daily T_dew using limited local and cross-station meteorological data. The random forests (RF) algorithm was also assessed for comparison. Daily meteorological data from 2016 to 2019, including maximum, minimum and average temperature (T_max, T_min and T_mean), maximum, minimum and average relative humidity (RH_max, RH_min and RH_mean), maximum, minimum and average global solar radiation (Rs_max, Rs_min and Rs_mean) from three weather stations in Hunan of China were used to evaluate the CatBoost and RF algorithms. The results showed that both algorithms achieved satisfactory estimation accuracy at the target stations (on average RMSE = 1.020°C, R²=0.969, MAE = 0.718°C and NRMSE = 0.087) in the absence of complete meteorological parameters (with only temperature data as input). The CatBoost algorithm (on average RMSE = 1.900°C and R²=0.835) was better than the RF algorithm (on average RMSE = 2.214°C and R²=0.828). The accuracy and stability of the CatBoost and RF algorithms were positively correlated with the number of input parameters, and the three-parameter algorithms achieved higher estimation accuracy than the two-parameter algorithms. The developed methodology is helpful to predict T_dew at regional scale.

In the process of weathering for a long time, due to the exchange of internal elements and environmental elements, the composition proportion of glass cultural relics changes, which affects the correct judgment of archeological workers on their categories. In this paper, by machine learning method, the random forest algorithm is used to find out the feature attributes with higher importance. Based on the results obtained by random forest, the decision tree algorithm is used to get the decision tree and the classification method of high-potassium glass and lead-barium glass. In addition, the clustering coefficients of high-potassium glass and lead-barium glass were calculated by clustering algorithm, and the number of subclasses of the two kinds of glass was calculated. Then the random forest and decision tree algorithms are used to get the classification methods of high-potassium glass subclass and lead-barium glass subclass.

Assessments of cognitive and verbal functions are widely used as screening tests to detect early dementia. This study developed an early dementia prediction model for Korean elderly based on random forest algorithm and compared its results and precision with those of logistic regression model and decision tree model. Subjects of the study were 418 elderly (135 males and 283 females) over the age of 60 in local communities. Outcome was defined as having dementia and explanatory variables included digit span forward, digit span backward, confrontational naming, Rey Complex Figure Test (RCFT) copy score, RCFT immediate recall, RCFT delayed recall, RCFT recognition true positive, RCFT recognition false positive, Seoul Verbal Learning Test (SVLT) immediate recall, SVLT delayed recall, SVLT recognition true positive, SVLT recognition false positive, Korean Color Word Stroop Test (K-CWST) color reading correct, and K-CWST color reading error. The Random Forests algorithm was used to develop prediction model and the result was compared with logistic regression model and decision tree based on chi-square automatic interaction detector (CHAID). As the result of the study, the tests with high level of predictive power in the detection of early dementia were verbal memory, visuospatial memory, naming, visuospatial functions, and executive functions. In addition, the random forests model was more accurate than logistic regression and CHIAD. In order to effectively detect early dementia, development of screening test programs is required which are composed of tests with high predictive power

Developing machine learning models for relative humidity prediction in air-based energy systems and environmental management applications

Context.As an increasing number of spectroscopic surveys become available, an automated approach to spectral classification becomes necessary. Due to the significance of the massive stars, it is of great importance to identify the phenomenological parameters of these stars (e.g., the spectral type), which can be used as proxies to their physical parameters (e.g., mass and temperature).Aims.In this work, we aim to use the random forest (RF) algorithm to develop a tool for the automated spectral classification of OB-type stars according to their sub-types.Methods.We used the regular RF algorithm, the probabilistic RF, which is an extension of RF that incorporates uncertainties, and we introduced the KDE – RF method which is a combination of the kernel-density estimation and the RF algorithm. We trained the algorithms on the equivalent width (EW) of characteristic absorption lines measured in high-quality spectra (signal-to-noise (S/N)≳50) from large Galactic (LAMOST, GOSSS) and extragalactic surveys (2dF, VFTS) with available spectral types and luminosity classes. By following an adaptive binning approach, we grouped the labels of these data in 11 spectral classes within the O2-B9 range. We examined which of the characteristic spectral lines (features) are more important for the classification based on a number of feature selection methods, and we searched for the optimal hyperparameters of the classifiers to achieve the best performance.Results.From the feature-screening process, we find that the full set of 17 spectral lines is needed to reach the maximum performance per spectral class. We find that the overall accuracy score is ∼70%, with similar results across all approaches. We apply our model in other observational data sets providing examples of the potential application of our classifier to real science cases. We find that it performs well for both single massive stars and for the companion massive stars in Be X-ray binaries, especially for data of similar quality to the training sample. In addition, we propose a reduced ten-features scheme that can be applied to large data sets with lowerS/N ∼ 20 − 50.Conclusions.The similarity in the performances of our models indicates the robustness and the reliability of the RF algorithm when it is used for the spectral classification of early-type stars. The score of ∼70% is high if we consider (a) the complexity of such multiclass classification problems (i.e., 11 classes), (b) the intrinsic scatter of the EW distributions within the examined spectral classes, and (c) the diversity of the training set since we use data obtained from different surveys with different observing strategies. In addition, the approach presented in this work is applicable to products from different surveys in terms of quality (e.g., different resolution) and different formats (e.g., absolute or normalized flux), while our classifier is agnostic to the luminosity class of a star, and, as much as possible, it is metallicity independent.

The power of Machine Learning is demonstrated for automatic interpretation of well logs and determining reservoir properties for volume of shale, porosity, and water saturation respectively for tight clastic sequences. Random Forest algorithms are reputed for their efficiency as they belong to a class of algorithms called ensemble methods, which are traditionally seen as weak learners, but can be transformed into strong performers and they promise to deliver highly accurate results. The study area is located offshore Australia in the Poseidon and Crown fields situated in the Browse Basin, which are gas fields in tight complex clastic reservoirs. There are 5 wells used in this study with one well manually interpreted which is subsequently used in developing a machine learning model which predicts the output for the other 4 wells. The basic open hole logs namely Natural gamma ray, Resistivity, Neutron Porosity, Bulk Density, P-wave and S-wave sonic travel-time, are used in interpretation. One of the wells has a missing S-wave travel-time log which was also predicted by developing a Random Forest Machine Learning model. The results indicate a very robust improvement in performance when Random Forest algorithm was combined with Adaptive Boosting when interpreting the well logs. The training accuracy using Random Forest alone was 98.21%, but testing was 77.62% which suggested over-fitting by the Random Forest model. The Adaptive Boosting of the Random Forest algorithm resulted in the overall training accuracy of 99.40% and an overall testing accuracy of 97.03%, indicating a drastic improvement in performance. S-wave travel-time log was predicted by preparing a training set consisting of Natural gamma ray, Resistivity, Neutron Porosity, Bulk Density, and P-wave travel-time logs for the 4 wells using Random Forest which gave a training accuracy of 99.79% and a testing accuracy of 98.54%. Machine learning algorithms can be successfully applied for interpreting well log data in complex sedimentary environment and their performance can be drastically improved using Adaptive Boosting.

Diagnostic and prognostic value of diquat plasma concentration and complete blood count in patients with acute diquat poisoning based on random forest algorithms.

With the continuous development of science and technology, the issue of learning copyright knowledge in music copyright education has become increasingly severe. In response to the problems highlighted above, this paper proposes a study on optimizing the effectiveness of copyright knowledge learning based on the random forest algorithm. By integrating music copyright education issues and three principles for constructing an evaluation system, this study establishes an evaluation indicator system for copyright knowledge learning effectiveness. Under the influence of the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation Method, the evaluation indicators are quantified. Based on this, the Random Forest algorithm is utilized to optimize the quantified data of each evaluation indicator, followed by an exploration of the optimization effects of the Random Forest algorithm. The optimization values of the four algorithms are 3.9365, 3.7213, 3.7323, and 3.7088, respectively. It can be concluded that the optimization effect of the RF (random forest) algorithm is higher than that of the DT (decision tree algorithm), LSTM (long short-term memory network), and LR (logistic regression), verifying the practical applicability and application value of the research scheme proposed in this paper, which has practical strategic significance for improving music copyright education.