Performance Of Random Forest Algorithm Research Articles

Prediction of the age of abalones based on machine learning algorithms

Abalone is an important seafood, widely used in food, medicine, and other fields. The age of abalone is one of the important factors that determine its quality and market value. However, the traditional age determination method requires the dissection of abalone, which is time-consuming and expensive. Therefore, it is important to find a fast and work out age prediction method. This article uses a machine learning algorithm to predict the age of abalone. The authors collected data on characteristics such as sex, length, diameter, height, and weight for 4177 abalone observations. This data set is admirably large. In the following study, the authors compare the effects of prediction using different machine learning algorithms, including linear regression, decision trees, random forests, and support vector machines. It is worth mentioning that the authors have done sufficient research and evaluation of these algorithms to find out the best prediction scheme. The results show that the random forest algorithm is the best, and its average absolute error is only 1.44 years. The performance of random forest algorithm is the best.

Evaluation of Risk Factors Associated With Antihypertensive Treatment Success Employing Data Mining Techniques.

This study aimed to evaluate the effects of potential risk factors on antihypertensive treatment success. Patients with hypertension who were treated with antihypertensive medications were included in this study. Data from the last visit were analyzed retrospectively for each patient. To evaluate the predictive models for antihypertensive treatment success, data mining algorithms (logistic regression, decision tree, random forest, and artificial neural network) using 5-fold cross-validation were applied. Additionally, study parameters between patients with controlled and uncontrolled hypertension were statistically compared and multiple regression analyses were conducted for secondary endpoints. The data of 592 patients were included in the analysis. The overall blood pressure control rate was 44%. The performance of random forest algorithm (accuracy = 97.46%, precision = 97.08%, F1 score = 97.04%) was slightly higher than other data mining algorithms including logistic regression (accuracy = 87.31%, precision = 86.21%, F1 score = 85.74%), decision tree (accuracy = 76.94%, precision = 70.64%, F1 score = 76.54%), and artificial neural network (accuracy = 86.47%, precision = 83.85%, F1 score = 84.86%). The top 5 important categorical variables (predictive correlation value) contributed the most to the prediction of antihypertensive treatment success were use of calcium channel blocker (-0.18), number of antihypertensive medications (0.18), female gender (0.10), alcohol use (-0.09) and attendance at regular follow up visits (0.09), respectively. The top 5 numerical variables contributed the most to the prediction of antihypertensive treatment success were blood urea nitrogen (-0.12), glucose (-0.12), hemoglobin A1c (-0.12), uric acid (-0.09) and creatinine (-0.07), respectively. According to the decision tree model; age, gender, regular attendance at follow-up visits, and diabetes status were identified as the most critical patterns for stratifying the patients. Data mining algorithms have the potential to produce predictive models for screening the antihypertensive treatment success. Further research on larger populations and longitudinal datasets are required to improve the models.

Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images

The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery.The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.

Enhancement performance of random forest algorithm via one hot encoding for IoT IDS

The random forest algorithm is one of important supervised machine learning (ML) algorithms. In the present paper, the accuracy of the results of the random forest (RF) algorithm has been improved by the use of the One Hot Encoding method. The Intrusion Detection System (IDS) can be defined as a system that can predict security vulnerabilities within network traffic and is located out of range on a network infrastructure. It does not affect the efficiency of the built-in network because it analyzes a copy of the built-in traffic flow and reports results to the administrator by giving alerts. However, since IDS is a listening system only, it cannot take automatic action to prevent an attack or security vulnerability detected from infecting the system, it provides information about the source address to start the break-in, the address of the target and the type of suspected attack. The IoTID20 dataset is used to verify the improved algorithm, where this dataset is having three targets, the proposed system is compared with the state-of-art approaches and shows superiority over them.

Identification and Prediction Methods of Financial Anti-fraud

Nowadays, evaluating and identifying the potential fraud risk of borrowers effectively and calculating the fraud probability of them are the basis and significant steps of credit risk management in modern financial institutions before issuing loans. This paper mainly studies the statical analysis of the historical loan data of financial institutions based on the idea of unbalanced data classification and establishes the prediction model of loan fraud through random forest, decision tree and regression algorithm. The prediction performance of random forest algorithm is better than the other two mentioned methods. Additionally, it may obtain the feature that have a remarkable impact on the final fraud by ranking the importance of those features, which leads to a more effective judgment on the credit risk in the financial field.

Prediction Performance of Lane Changing Behaviors: A Study of Combining Environmental and Eye-Tracking Data in a Driving Simulator

Advanced Driver Assistance Systems (ADAS) are systems developed to assist the human driver and therefore to make driving safer and better. Understanding and predicting human driving behavior play an important role in the development of assistance systems. In this contribution, the development of a driver assistance system is based on the prediction of driving behaviors. The driving patterns of three different behaviors are modeled including left/right lane change and lane keeping. A driving simulator is used to simulate a highway scene. The implementation of a prediction system based on different machine learning approaches such as Hidden Markov Model (HMM), Support Vector Machine (SVM), Convolutional neural networks (CNNs), and Random Forest (RF) is accomplished. In addition, eye-tracking information is integrated. The task is to predict behaviors based on the measurement. As test, a 10-fold cross-validation is used based on data sets from driving simulator and applied to compare the performance of different algorithms. In combination with related results in terms of accuracy (ACC), detection rate (DR), and false alarm rate (FAR), the performance and effectiveness of the developed prediction systems are evaluated. The results show that the performance of RF algorithm is the best of all four algorithms compared. Combining environmental and eye-tracking data the RF algorithm achieved the best results. All ACC values are larger than 99 %. Afterwards, two RF-based prediction models with and without eye-tracking data are developed for online test. Finally, some application samples are suggested for driver assistance. The results calculated by the proposed model are shown on a user interface to help the drivers to see when it is suitable to turn left, to turn right, or to keep the direction.

An improved random forest classifier for multi-class classification

The paper presents an improved-RFC (Random Forest Classifier) approach for multi-class disease classification problem. It consists of a combination of Random Forest machine learning algorithm, an attribute evaluator method and an instance filter method. It intends to improve the performance of Random Forest algorithm. The performance results confirm that the proposed improved-RFC approach performs better than Random Forest algorithm with increase in disease classification accuracy up to 97.80% for multi-class groundnut disease dataset. The performance of improved-RFC approach is tested for its efficiency on five benchmark datasets. It shows superior performance on all these datasets.

Systematic analysis revealed better performance of random forest algorithm coupled with complex network features in predicting microRNA precursors

The improvement of computational methods greatly benefits the investigation of miRNAs. Our study validates the features in miRNA identification with an independent dataset, and provides researchers with common practices of the development of predictive models. A total number of 84 representative features, which occurred in researches of miRNAs classification, have been extracted and divided into four feature sets, i.e. complex network feature set (NET), structural feature set (STRUC), thermodynamic feature set (THERMO), and hybrid feature set (TOTAL). Systematic analysis is carried out on network, structural, thermodynamic and hybrid features. The dominant features are discriminated from uninformative features in both single and hybrid sets, on the basis of permutation importance strategy. Random forest models are constructed using only informative network, structural, thermodynamic and hybrid variables, resulting in area under the receiver operating curve (AUC) values of 0.9611, 0.9563, 0.9351, and 0.9469, respectively, based on validated datasets. The result suggests that the best performance could be got by using features derived from complex network. These results would be invaluable in understanding biological mechanism and function of miRNAs. All the data and scripts used in this article are freely available for download at http://cic.scu.edu.cn/bioinformatics/Extended_miRNA.zip.