Enhancing Random Forest Performance through Optimal Instance Subset Selection using Genetic Algorithm

Dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation

In tissue diagnosis with microarray data, selecting the optimal gene subset is very essential for various merits. Different from existing methods, we propose a novel optimal gene subset selection method based on genetic algorithms (GAs). Special fitness function is applied in this scheme. This GA-based method automatically determines the size of a predictive gene group, as well as the members in such a group. The evaluation experiments are applied to two popular data sets. The results and some discussions are presented too.

Growth in system complexity increases the need for automated techniques dedicated to different log analysis tasks such as Log-based Anomaly Detection (LAD). The latter has been widely addressed in the literature, mostly by means of a variety of deep learning techniques. However, despite their many advantages, that focus on deep learning techniques is somewhat arbitrary as traditional Machine Learning (ML) techniques may perform well in many cases, depending on the context and datasets. In the same vein, semi-supervised techniques deserve the same attention as supervised techniques since the former have clear practical advantages. Further, current evaluations mostly rely on the assessment of detection accuracy. However, this is not enough to decide whether or not a specific ML technique is suitable to address the LAD problem in a given context. Other aspects to consider include training and prediction times as well as the sensitivity to hyperparameter tuning, which in practice matters to engineers. In this paper, we present a comprehensive empirical study, in which we evaluate a wide array of supervised and semi-supervised, traditional and deep ML techniques w.r.t. four evaluation criteria: detection accuracy, time performance, sensitivity of detection accuracy and time performance to hyperparameter tuning. Our goal is to provide much stronger and comprehensive evidence regarding the relative advantages and drawbacks of alternative techniques for LAD. The experimental results show that supervised traditional and deep ML techniques fare similarly in terms of their detection accuracy and prediction time on most of the benchmark datasets considered in our study. Moreover, overall, sensitivity analysis to hyperparameter tuning with respect to detection accuracy shows that supervised traditional ML techniques are less sensitive than deep learning techniques. Further, semi-supervised techniques yield significantly worse detection accuracy than supervised techniques.

Rapid and accurate ambiguity resolution is the core of high-precision precise point positioning (PPP) data processing. However, the ambiguity parameters in PPP observation models are easily affected by atmospheric residual and gross errors, which lead to the probability of successfully fixing decreases and computational burden increases in full ambiguity resolution. Therefore, an increasing number of partial ambiguity resolution (PAR) strategies have been proposed. The selection of the optimal subset of PAR is crucial in this method. The traditional optimal subset selection method of PAR commonly leads to a single judgment criterion and weakened geometric configuration strength because the satellites with low elevation angles are often easily eliminated during the optimal subset selection. In this paper, a multi-factor constrained optimal subset selection method for PAR was proposed, which incorporates the ambiguity variance, the ambiguity dilution of precision (ADOP), satellite position dilution of precision (PDOP) and ratio test values. In order to verify the feasibility of the proposed optimal subset selection method, PAR tests under two schemes were performed for GPS/Galileo based on the static observation data of 15 Multi-GNSS Experiment (MGEX) tracking stations. The results show that, compared with the ambiguity variance sorting method, the proposed subset selection method can further improve the accuracy of the coordinate solution and the strength of geometric figure positioning. The average root mean square of the coordinate residuals is found to decrease by about 12.90%, 6.83% and 9.39% in the eastern, northern and vertical directions, respectively. The increase in the fixed epoch rate ranged from 0.87% to 33.33%, with an average of about 8.71%.

An approach combining optimal antenna subset selection with blind detection scheme for Orthogonal Space-Time Block Coding (OSTBC) is proposed in this paper. The optimal antenna subset selection is taken into account at transmitter and/or receiver sides, which chooses the optimal antennas to increase the diversity order of OSTBC and improve further its performance. In order to enhance the robustness of the detection used in the conventional OSTBC scheme, a blind detection scheme based on Independent Component Analysis (ICA) is exploited which can directly extract transmitted signals without channel estimation. Performance analysis shows that the proposed approach can achieve the full diversity and the flexibility of system design by using the antenna selection and the ICA based blind detection schemes.

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.

This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriateness of nursing care delivery models in hospital wards. The primary outcome measures include the adequacy of nurse staffing and the appropriateness of the nursing care delivery system. Historical and current healthcare challenges, such as nurse shortages and increasing patient acuity, necessitate innovative approaches to nursing care delivery. For instance, the COVID-19 pandemic highlighted the need for flexible and scalable staffing models to manage surges in patient volume and acuity. A descriptive study was conducted in 39 inpatient wards across a university hospital and three state hospitals, involving 117 ward-level observations. Data were collected using the Rush Medicus Patient Classification Scale and analysed using k-Nearest Neighbour, Support Vector Machine, Random Forest, and Logistic Regression algorithms. Effectiveness was measured by the accuracy of machine learning predictions regarding nurse staffing adequacy, while suitability was determined by the congruence between observed nursing care models and patient needs. STROBE checklist. The Random Forest algorithm demonstrated the highest accuracy in predicting both nurse staffing adequacy and the appropriateness of nursing care delivery systems. The study found that 68.4% of wards had sufficient nurse staffing and 26.5% of wards used appropriate care delivery models, with functional nursing and total patient care models being the most commonly used. The study highlights functional nursing and total patient care models, emphasising the need to consider nurse qualifications and patient needs in selecting care systems. Machine learning, particularly the Random Forest algorithm, proved effective in aligning staffing with patient requirements. Machine learning, particularly the Random Forest algorithm, proves effective in optimising nursing care delivery models, suggesting significant potential for enhancing patient care and nurse satisfaction. The research underscores machine learning's role in improving nursing care delivery, aligning nurse staffing with patient needs, and advancing healthcare outcomes. The findings advocate for integrating machine learning in the planning of nursing care delivery models. This study sets a precedent for using data-driven approaches to improve nurse staffing and care delivery, potentially enhancing global clinical outcomes and operational efficiencies. The global clinical community can learn from this study the value of employing machine learning techniques to make informed, evidence-based decisions in healthcare management. While the study lacked direct patient involvement, its goal was to enhance patient care and healthcare efficiency. Future research will aim to incorporate patient and public insights more directly.

The aim of this paper is to develop an effective classification approach based on Random Forest (RF) algorithm. Three fruits; i.e., apples, Strawberry, and oranges were analysed and several features were extracted based on the fruits' shape, colour characteristics as well as Scale Invariant Feature Transform (SIFT). A preprocessing stages using image processing to prepare the fruit images dataset to reduce their color index is presented. The fruit image features is then extracted. Finally, the fruit classification process is adopted using random forests (RF), which is a recently developed machine learning algorithm. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB environment. Experiments were tested and evaluated using a series of experiments with 178 fruit images. It shows that Random Forest (RF) based algorithm provides better accuracy compared to the other well know machine learning techniques such as K-Nearest Neighborhood (K-NN) and Support Vector Machine (SVM) algorithms. Moreover, the system is capable of automatically recognize the fruit name with a high degree of accuracy.

Candidemia is a highly lethal infection; several scores have been developed to assist the diagnosis process and recently different models have been proposed. Aim of this work was to assess predictive performance of a Random Forest (RF) algorithm for early detection of candidemia in the internal medical wards (IMWs). A set of 42 potential predictors was acquired in a sample of 295 patients (male: 142, age: 72 ± 15years; candidemia: 157/295; bacteremia: 138/295). Using tenfold cross-validation, a RF algorithm was compared with a classic stepwise multivariable logistic regression model; discriminative performance was assessed by C-statistics, sensitivity and specificity, while calibration was evaluated by Hosmer-Lemeshow test. The best tuned RF algorithm demonstrated excellent discrimination (C-statistics = 0.874 ± 0.003, sensitivity = 84.24% ± 0.67%, specificity = 91% ± 2.63%) and calibration (Hosmer-Lemeshow statistics = 12.779 ± 1.369, p = 0.120), markedly greater than the ones guaranteed by the classic stepwise logistic regression (C-statistics = 0.829 ± 0.011, sensitivity = 80.21% ± 1.67%, specificity = 84.81% ± 2.68%; Hosmer-Lemeshow statistics = 38.182 ± 15.983, p < 0.001). In addition, RF suggests a major role of in-hospital antibiotic treatment with microbioma highly impacting antimicrobials (MHIA) that are found as a fundamental risk of candidemia, further enhanced by TPN. When in-hospital MHIA therapy is not performed, PICC is the dominant risk factor for candidemia, again enhanced by TPN. When PICC is not used and MHIA therapy is not performed, the risk of candidemia is minimum, slightly increased by in-hospital antibiotic therapy. RF accurately estimates the risk of candidemia in patients admitted to IMWs. Machine learning technique might help to identify patients at high risk of candidemia, reduce the delay in empirical treatment and improve appropriateness in antifungal prescription.

Several machine learning (ML) techniques were tested for the feasibility of performing automated pattern and waveform recognitions of terahertz time-domain spectroscopy datasets. Out of all the ML techniques under test, it was observed that random forest statistical algorithm works well with the THz datasets in both the frequency and time domains. With such ML algorithm, a classifier can be created with less than 1% out-of-bag error for segmentation of rusted and non-rusted sample regions of the image datasets in frequency domain. The degree of linear correlation between the rusted area percentage and the image spatial resolution with terahertz frequency can be used as an additional cross-validation criteria for the evaluation of classifier quality. However, for different rust staging measured datasets, a standardized procedure of image pre-processing is necessary to create/apply a single classifier and its usage is only limited to 1 ± 0.2 THz. Moreover, random forest is practically the best choice among the several popular ML techniques under test for waveform recognition of time-domain data in terms of classification accuracy and timing. Our results demonstrate the usefulness of random forest and several other machine learning algorithms for terahertz hyperspectral pattern recognition.

Electrocardiogram is a diagnostic tool that makes a record of the muscular and electrical activities of the heart for showing that data as a trace on a piece of paper, which is then carefully studied and interpreted by a clinical assistant. Classification of ECG signals on performance factors like sensitivity, specificity and accuracy using machine learning techniques can provide substantial input to doctors for essential and effective treatment of the patient. Arrhythmia refers to the condition when the heart beats improperly. Irregular, fast or slow beating of heart comes under arrhythmia. It is caused due to improper working of electrical impulses of the heart. In this research work, various machine learning techniques were made use for the purpose of classification of normal and arrhythmic beats. The major objective of this work is automated classification of normal and irregular beats. Introduced approach deals with a good volume of standard ECG time-series data as inputs to various machine learning classifiers such as Naive Bayes, support vector machine, ada-boost, random forest, decision tree and k-nearest neighbor classifiers. MIT-BIH arrhythmia database has been made use for the purpose of examining the best classification performance, and one of the most popular datasets of the same database has been taken for training and testing purposes. This comprises of an exact 47 records of thirty minutes each and forty percent of the entire records were those of cardiac patients. Three major performance evaluation parameters have been examined by the researchers on distinct machine learning algorithms. They are accuracy, sensitivity and specificity. A confusion matrix has been made use for describing all these measurements as false positive [FP], false negative [FN], true positive [TP] and true negative [TN]. The efficiency and potential of different methods of arrhythmia detection are demonstrated, and numerous comparisons with various other conventional machine learning techniques are also made. The researchers calculated the sensitivity, specificity and accuracy and found the best result using decision tree classifier with 88.2% accuracy.

Instance selection plays an important role in improving scalability of data mining algorithms, but it can also be used to improve the quality of the data mining results. In this dissertation we present a new optimization-based approach for instance selection that uses a genetic algorithm (GA) to select a subset of instances to produce a simpler decision tree with acceptable accuracy. The resultant trees are likely to be easier to comprehend and interpret by the decision maker and hence more useful in practice. We present numerical results for several difficult test datasets that indicate that GA-based instance selection can often reduce the size of the decision tree by an order of magnitude while still maintaining good prediction accuracy. The results suggest that GA-based instance selection works best for low entropy datasets. With higher entropy, there will be less benefit from instance selection. A comparison between GA and other heuristic approaches such as Rmhc (Random Mutation Hill Climbing) and simple construction heuristic, indicates that GA is able to obtain a good solution with low computation cost even for some large datasets. One advantage of instance selection is that it is able to increase the average instances associated with the leaves of the decision trees to avoid overfitting, thus instance selection can be used as an effective alternative to prune decision trees. Finally, the analysis on the selected instances reveals that instance selection helps to reduce outliers, reduce missing values, and select the most useful instances for separating classes.

To enhance the safety and reliability of fuel cell vehicles, a remote monitoring system based on 5th generation (5G) mobile networks and controller area networks (CANs) was designed, and a random forest (RF) algorithm for the fault diagnosis for eight typical malfunctions of its powertrain system was incorporated. Firstly, the information on the powertrain system was obtained through a 5G-based monitoring terminal, and the Alibaba Cloud IoT platform was utilized for data storage and remote monitoring. Secondly, a fault diagnosis model based on the RF algorithm was constructed for fault classification; its parameters were optimized with a genetic algorithm (GA), and it was applied on the Alibaba Cloud PAI platform. Finally, the performance of the proposed RF fault diagnosis model was evaluated by comparing it with three other classification models: random search conditioning, grid search conditioning, and Bayesian optimization. Results show that the model accuracy, F1 score, and kappa value of the optimized RF fault classification model are higher than the other three. The model achieves an F1 value of 97.77% in identifying multiple typical faults of the powertrain system, as validated by vehicle malfunction data. The method demonstrates the feasibility of remote monitoring and fault diagnosis for the powertrain system of fuel cell vehicles.

PurposeThis study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.Design/methodology/approachA data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.FindingsAmong five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.Originality/valueAddressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

In this paper, an artificial intelligence-based algorithm for predicting the optimal contrast medium dose for computed tomography (CT) angiography of the aorta is presented and evaluated in a clinical study. The prediction of the contrast dose reduction is modelled as a classification problem using the image contrast as the main feature. This classification is performed by random decision forests (RDF) and k-nearest-neighbor methods (KNN). For the selection of optimal parameter subsets all possible combinations of the 22 clinical parameters (age, blood pressure, etc.) are considered using the classification accuracy and precision of the KNN classifier and RDF as quality criteria. Subsequently, the results of the evaluation were optimized by means of feature transformation using regression neural networks (RNN). These were used for a direct classification based on regressed Hounsfield units as well as preprocessing for a subsequent KNN classification. For feature selection, an RDF model achieved the highest accuracy of 84.42% and a KNN model achieved the best precision of 86.21%. The most important parameters include age, height, and hemoglobin. The feature transformation using an RNN considerably exceeded these values with an accuracy of 90.00% and a precision of 97.62% using all 22 parameters as input. However, also the feasibility of the parameter sets in routine clinical practice has to be considered, because some of the 22 parameters are not measured in routine clinical practice and additional measurement time of 15 to 20 minutes per patient is needed. Using the standard feature set available in clinical routine the best accuracy of 86.67% and precision of 93.18% was achieved by the RNN. We developed a reliable hybrid system that helps radiologists determine the optimal contrast dose for CT angiography based on patient-specific parameters.